Publications

@article{MARTINEZ24,

title = {Emerging trends in big data analytics and natural disasters (Editorial)},

author = {F. Martínez-Álvarez and R. Scitovski and C. Rubio-Escudero and A. Morales-Esteban},

url = {https://www.sciencedirect.com/science/article/pii/S0098300423001693},

doi = {https://doi.org/10.1016/j.cageo.2023.105465},

year  = {2024},

date = {2024-01-01},

journal = {Computers and Geosciences},

volume = {182},

pages = {105465},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MAZARI23,

title = {Creating a homogenized earthquake catalog for Algeria and mapping the main seismic parameters using a geographic information system},

author = {O. S. Mazari and A. Sebaa and J. L. Amaro-Mellado and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S1464343X23000687},

doi = {https://doi.org/10.1016/j.jafrearsci.2023.104895},

year  = {2023},

date = {2023-03-03},

journal = {Journal of African Earth Sciences},

volume = {201},

pages = {104859},

abstract = {A homogeneous earthquake catalog is an essential instrument to study earthquake occurrence patterns, employing diverse engineering applications. In this paper, we describe a series of compilation and processing steps to compile an updated earthquake catalog for Algeria, a North African country with relatively high seismic activity. The procedure consisted of several steps. First, a range of reliable catalogs were considered; second, the data was integrated and refined; third, magnitudes are homogenized from different kinds of magnitudes into moment magnitude (M_w); declustering is then performed; and, finally, the magnitude-year completeness was estimated. The resulting Algeria catalog is bounded by the geographical limits (19° - 38.5° N and 9.5° W - 12.5° E), and covers the 1960-2020 period. It includes 4021 seismic events, reported up to M_w 7.1. We also calculate a set of seismic parameters, namely M_max and b-value, and mapped them using a geographic information system. Thus, the territory is divided into cells based on different grids to conduct the analysis. The results of the seismic parameters mapping are discussed, highlighting significant details. Several cells presented a M_max between 6.0 and 7.1. Regarding the b-value, two regions (Oran and Constantine) presented a high b-value, implying low-stress areas, and three regions (Algiers, Batna, and Chlef) a low b-value (0.65- 0.85), suggesting high-stress areas. Finally, we suggest some recommendations for future seismic hazard assessment studies.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{Melgar2023c,

title = {A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area},

author = {L. Melgar-García and F. Martínez-Álvarez and D. T. Bui and A. Troncoso},

url = {https://www.tandfonline.com/doi/full/10.1080/17538947.2023.2252401},

doi = {https://doi.org/10.1080/17538947.2023.2252401},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Digital Earth},

volume = {16},

number = {1},

pages = {3661-3679},

keywords = {deep learning, natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MORALES21,

title = {Mahalanobis clustering for the determination of incidence-magnitude seismic parameters for the Iberian Peninsula and the Republic of Croatia},

author = {A. Morales-Esteban and F. Martínez-Álvarez and S. Scitovski and R. Scitovski},

url = {https://www.sciencedirect.com/science/article/pii/S0098300421001667},

doi = {https://doi.org/10.1016/j.cageo.2021.104873},

year  = {2021},

date = {2021-07-09},

journal = {Computers and Geosciences},

volume = {156},

pages = {104873},

abstract = {The aim of this paper is to analyse the seismic activity of the Iberian Peninsula and a wide area of the Republicof Croatia. To do so, two incidence-magnitude seismic parameters have been defined. First, the areas have beendivided into several ellipsoidal clusters using Mahalanobis clustering. Four generalised indexes (Mahalanobis Calinski Harabasz, Mahalanobis Davies–Bouldin, Mahalanobis Simplified Silhouette Width Criterion and Mahalanobis Area) have been used to determine the most appropriate number of ellipsoidal clusters, on the basis of which a partition with four and a partition with eleven clusters have been considered. For the widearea of the Republic of Croatia there are fourteen clusters and the five areas that just affect Croatia have been analysed in detail. Then, to analyse the seismic activity of the areas, two incidence-magnitude seismic parameters have been defined and calculated: a) 𝛥(4), that represents the minimal number of successive years in which at least one earthquake of magnitude between 4 and 5 has been registered; b) 𝛥(5), that shows the number of years in which at least one earthquake of magnitude larger than 5 occurred. The calculation of 𝛥(4) for the South-west and the South-east of the Iberian Peninsula has provided two years for both. Regarding 𝛥(5), 10 and 12 years have been obtained for the South-west and the South-east of the Iberian Peninsula,respectively. The analysis of Croatia has shown that the Ston–Metković area has the highest seismic activity. The following results have been determined: 5 years for 𝛥(4) and 22 for 𝛥(5). It should be mentioned that the seresults cannot be used for predicting earthquakes. However, data about the incidences of earthquake events and their magnitudes can certainly serve as useful information in civil engineering.},

keywords = {natural disasters, pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@article{ASIM20d,

title = {Seismicity Analysis and Machine Learning Models for Short-Term Low Magnitude},

author = {K. Asim and E Elawadi and F. Martínez-Álvarez and I. A. Niaz and S. R. M. Sayed and T. Iqbal},

url = {https://www.sciencedirect.com/science/article/pii/S0267726119302192},

doi = {https://doi.org/10.1016/j.soildyn.2019.105932},

year  = {2020},

date = {2020-03-01},

journal = {Soil Dynamics and Earthquake Engineering},

volume = {130},

pages = {id105932},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{BUI20,

title = {A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area},

author = {D. T. Bui and N.-D. Hoang and F. Martínez-Álvarez and P.-T. T. Ngo and P. V. Hoa and T. D. Pham and P. Samui and R. Costache},

url = {https://www.sciencedirect.com/science/article/pii/S0048969719344043},

doi = {https://doi.org/10.1016/j.scitotenv.2019.134413},

year  = {2020},

date = {2020-01-20},

journal = {Science of the Total Environment},

volume = {701},

pages = {id134413},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ20c,

title = {Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management (Editorial)},

author = {F. Martínez-Álvarez and D. T. Bui},

url = {https://www.mdpi.com/2072-4292/12/2/301},

doi = {10.3390/rs12020301},

issn = {2072-4292},

year  = {2020},

date = {2020-01-01},

journal = {Remote Sensing},

volume = {12},

number = {2},

pages = {301},

abstract = {This editorial summarizes the performance of the special issue entitled Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management, which was published at MDPI’s Remote Sensing journal. The special issue took place in years 2018 and 2019 and accepted a total of nine papers from authors of thirteen different countries. So far, these papers have dealt with 116 cites. Earthquakes, landslides, floods, wildfire and soil salinity were the topics analyzed. New methods were introduced, with applications of the utmost relevance},

keywords = {big data, natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ19,

title = {Big data and natural disasters: New approaches for temporal and spatial massive data analysis (Editorial)},

author = {F. Martínez-Álvarez and A. Morales-Esteban},

url = {https://www.sciencedirect.com/science/article/pii/S009830041930411X?dgcid=rss_sd_all},

doi = {https://doi.org/10.1016/j.cageo.2019.04.012},

year  = {2019},

date = {2019-08-01},

journal = {Computers and Geosciences},

volume = {129},

pages = {38-39},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TEHRANY19,

title = {A Novel Ensemble Modelling Approach for the Spatial Prediction of Tropical Forest Fire Susceptibility Using Logitboost Machine Learning Classifier and Multi-source Geospatial Data},

author = {M. S. Tehrany and S. Jones and F. Shabani and F. Martínez-Álvarez and D. T. Bui},

url = {https://link.springer.com/article/10.1007/s00704-018-2628-9},

doi = {https://doi.org/10.1007/s00704-018-2628-9},

year  = {2019},

date = {2019-01-01},

journal = {Theoretical and Applied Climatology},

volume = {137},

pages = {637-653},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Tehrany2018,

title = {A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data},

author = {M. S. Tehrany and S. Jones and F. Shabani and F. Martínez-Álvarez and D. Tien Bui},

url = {https://link.springer.com/article/10.1007/s00704-018-2628-9},

doi = {10.1007/s00704-018-2628-9},

year  = {2018},

date = {2018-01-01},

journal = {Theoretical and Applied Climatology},

abstract = {A reliable forest fire susceptibility map is a necessity for disaster management and a primary reference source in land use planning. We set out to evaluate the use of the LogitBoost ensemble-based decision tree (LEDT) machine learning method for forest fire susceptibility mapping through a comparative case study at the Lao Cai region of Vietnam. A thorough literature search would indicate the method has not previously been applied to forest fires. Support vector machine (SVM), random forest (RF), and Kernel logistic regression (KLR) were used as benchmarks in the comparative evaluation. A fire inventory database for the study area was constructed based on data of previous forest fire occurrences, and related conditioning factors were generated from a number of sources. Thereafter, forest fire probability indices were computed through each of the four modeling techniques, and performances were compared using the area under the curve (AUC), Kappa index, overall accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). The LEDT model produced the best performance, both on the training and on validation datasets, demonstrating a 92% prediction capability. Its overall superiority over the benchmarking models suggests that it has the potential to be used as an efficient new tool for forest fire susceptibility mapping. Fire prevention is a critical concern for local forestry authorities in tropical Lao Cai region, and based on the evidence of our study, the method has a potential application in forestry conservation management.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{Florido2018,

title = {A novel tree-based algorithm to discover seismic patterns in earthquake catalogs},

author = {E. Florido and G. Asencio-Cortes and J. L. Aznarte and C. Rubio-Escudero and F. Martinez-Alvarez},

doi = {10.1016/j.cageo.2018.03.005},

issn = {0098-3004},

year  = {2018},

date = {2018-01-01},

journal = {Computers and Geosciences},

number = {115},

pages = {96-104},

abstract = {A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Amaro-Mellado2018,

title = {Mapping of seismic parameters of the Iberian Peninsula by means of a geographic information system},

author = {J. L. Amaro-Mellado and A. Morales-Esteban and F. Martínez-Álvarez},

url = {https://link.springer.com/article/10.1007/s10100-017-0506-7},

doi = {10.1007/s10100-017-0506-7},

year  = {2018},

date = {2018-01-01},

journal = {Central European Journal of Operations Research},

volume = {26},

number = {3},

pages = {739-758},

abstract = {In this paper, the following seismic parameters, the maximum recorded magnitude (Mmax), the Gutenberg--Ritcher b-value and the (normalized) mean seismic activity rate, AR, have been calculated for the Iberian Peninsula and surroundings. A geographic information system has been employed to compile all data, to work with different geographic systems and to generate the maps. An improved version of the National Geographic Institute of Spain earthquake catalog has been considered as input. Due to the detection network evolution and the extent of the territory, completeness values must be sectored to obtain reliable b-values and AR values. So, a previous work on regionalization has been considered. First, a working catalog has been elaborated. To do so, the size of some shocks through specific studies have been reviewed, magnitudes have been converted to moment magnitude (Mw) and dependent events have been removed. Second, for the b-value and the AR calculation a method that considers inhomogeneous catalogs, different magnitudes and various years of completeness has been employed. Also, different decluster parameters and various minimum number of events have been considered. Finally, to represent the values, a multi-resolution grid (0.5ºx0.5º, 1ºx1º and 2ºx2º) has been deployed. The pictures obtained show the seismicity in the terms of size, stress-meter and frequency. The highest $$M_max$$Mmaxhas been obtained in the SW of the Iberian Peninsula with a marine epicenter. Regarding the b-value, in the contact boundary between the Africa and Eurasian plates this value is around 1.0 or minor. Contrariwise, in the mainland, values higher than 1.2 are predominant. Finally, the highest AR values are found in the SE of the Iberian Peninsula and remarkable values are also present in the NE.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{Asencio-Cortes2018b,

title = {Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure},

author = {G. Asencio-Cortes and A. Morales-Esteban and X. Shang and F. Martinez-Alvarez},

doi = {10.1016/j.cageo.2017.10.011},

issn = {0098-3004},

year  = {2018},

date = {2018-01-01},

journal = {Computers and Geosciences},

number = {115},

pages = {198-210},

abstract = {Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades. Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satisfactory results. In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets. These new methods make use of physical resources like cloud based architectures. California is known for being one of the regions with highest seismic activity in the world and many data are available. In this work, the use of several regression algorithms combined with ensemble learning is explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the next seven days. Apache Spark framework, H2O library in R language and Amazon cloud infrastructure were been used, reporting very promising results.},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Shang2018b,

title = {Data field-based K-means clustering for spatio-temporal seismicity analysis and hazard assessment},

author = {X. Shang and X. Li and A. Morales-Esteban and G. Asencio-Cortes and Z. Wang},

doi = {10.3390/rs10030461},

issn = {2072-4292},

year  = {2018},

date = {2018-01-01},

journal = {Remote Sensing},

volume = {10},

number = {461},

pages = {1-22},

abstract = {Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts' seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ASIM20181,

title = {Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification},

author = {K. Asim and A. Idris and T. Iqbal and F. Martínez-Álvarez},

url = {http://www.sciencedirect.com/science/article/pii/S0267726118301349},

doi = {10.1016/j.soildyn.2018.04.020},

year  = {2018},

date = {2018-01-01},

journal = {Soil Dynamics and Earthquake Engineering},

volume = {111},

pages = {1-7},

abstract = {In this study an earthquake predictor system is proposed by combining seismic indicators along with Genetic Programming (GP) and AdaBoost (GP-AdaBoost) based ensemble method. Seismic indicators are computed through a novel methodology in which, the indicators are computed to obtain maximum information regarding seismic state of the region. The computed seismic indicators are used with GP-AdaBoost algorithm to develop an Earthquake Predictor system (EP-GPBoost). The setup has been arranged to provide predictions for earthquakes of magnitude 5.0 and above, fifteen days prior to the earthquake. The regions of Hindukush, Chile and Southern California are considered for experimentation. The EP-GPBoost has produced noticeable improvement in earthquake prediction due to collaboration of strong searching and boosting capabilities of GP and AdaBoost, respectively. The earthquake predictor system shows enhanced results in terms of accuracy, precision and Matthews Correlation Coefficient for the three considered regions in comparison to contemporary results.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{Amaro-Mellado2017,

title = {Comparing seismic parameters for different source zone models in the Iberian Peninsula},

author = {J. L. Amaro-Mellado and A. Morales-Esteban and G. Asencio-Cortes and F. Martinez-Alvarez},

doi = {10.1016/j.tecto.2017.08.032},

issn = {0040-1951},

year  = {2017},

date = {2017-01-01},

journal = {Tectonophysics},

number = {717},

pages = {449-472},

abstract = {Seismical parameters of five seismogenic zonings for the Iberian Peninsula have been determined in this work. For that purpose, this research has two key goals. The first is to generate a seismic catalog. The second to calculate the seismical parameters of all the zones of the seismogenic zonings selected. The first key goal has been the creation of a catalog of earthquakes for the Iberian Peninsula and adjacent areas. First, the National Geographic Institute of Spain's catalog has been completed and reviewed with the information from other catalog reviews and specific studies. Second, all magnitude calculations have been homogenized. Third, all dependent data have been eliminated through declustering. Finally, the year of completeness for each magnitude has been considered. The Quaternary active faults database of Iberia has also been used as input data. All of this information has been integrated into a geographic information system. The second key aim is the calculation of the seismical parameters. The first parameter obtained has been the b-value. A method which considers different years of completeness in accordance with the magnitude has been used. Also, the annual rate of earthquakes per square kilometer has been calculated. Moreover, the maximum magnitude known that Quaternary active faults might generate and maximum magnitude recorded in the catalog have been determined. Finally, based solely on the statistical parameters obtained, a critical discussion of the seismogenic zonings of the Iberian Peninsula has been conducted. The results show that some zonings possess insufficient data for a proper calculation of the seismic parameters, from a statistical point of view.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Asim20172,

title = {Earthquake magnitude prediction in Hindukush region using machine learning techniques},

author = {K. Asim and F. Martínez-Álvarez and A. Basit and T. Iqbal},

url = {https://link.springer.com/article/10.1007/s11069-016-2579-3},

doi = {10.1007/s11069-016-2579-3},

year  = {2017},

date = {2017-01-01},

journal = {Natural Hazards},

volume = {85},

number = {1},

pages = {471-486},

abstract = {Earthquake magnitude prediction for Hindukush region has been carried out in this research using the temporal sequence of historic seismic activities in combination with the machine learning classifiers. Prediction has been made on the basis of mathematically calculated eight seismic indicators using the earthquake catalog of the region. These parameters are based on the well-known geophysical facts of Gutenberg--Richter's inverse law, distribution of characteristic earthquake magnitudes and seismic quiescence. In this research, four machine learning techniques including pattern recognition neural network, recurrent neural network, random forest and linear programming boost ensemble classifier are separately applied to model relationships between calculated seismic parameters and future earthquake occurrences. The problem is formulated as a binary classification task and predictions are made for earthquakes of magnitude greater than or equal to 5.5 (M>=5.5), for the duration of 1 month. Furthermore, the analysis of earthquake prediction results is carried out for every machine learning classifier in terms of sensitivity, specificity, true and false predictive values. Accuracy is another performance measure considered for analyzing the results. Earthquake magnitude prediction for the Hindukush using these aforementioned techniques show significant and encouraging results, thus constituting a step forward toward the final robust prediction mechanism which is not available so far.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{APSCI2017,

title = {Large earthquake magnitude prediction in Chile with imbalanced classifiers and ensemble learning},

author = {M. J. Fernández-Gómez and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.mdpi.com/2076-3417/7/6/625},

doi = {10.3390/app7060625},

year  = {2017},

date = {2017-01-01},

journal = {Applied Sciences},

volume = {7},

number = {6},

pages = {625},

abstract = {This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{NCA2017,

title = {Medium-Large earthquake magnitude prediction in Tokyo with artificial neural networks},

author = {G. Asencio-Cortés and F. Martínez-Álvarez and A. Troncoso and A. Morales-Esteban},

url = {http://link.springer.com/article/10.1007/s00521-015-2121-7},

doi = {10.1007/s00521-015-2121-7},

year  = {2017},

date = {2017-01-01},

journal = {Neural Computing and Applications},

volume = {28},

number = {5},

pages = {1043-1055},

abstract = {This work evaluates artificial neural networks’ accuracy when used to predict earthquakes magnitude in Tokyo. Several seismicity indicators have been retrieved from the literature and used as input for the networks. Some of them have been improved and parameterized in order to extract more valuable knowledge from datasets. The experimental set-up includes predictions for five consecutive datasets referring to year 2015, earthquakes with magnitude larger than 5.0 and for a temporal horizon of seven days. Results have been compared to four well-known machine learning algorithms, reporting very promising results in terms of all quality parameters evaluated. The statistical tests applied conclude that differences between the proposed artificial neural network and the other methods are significant.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{AsencioCortes2017,

title = {Temporal analysis of croatian seismogenic zones to improve earthquake magnitude prediction},

author = {G. Asencio-Cortes and S. Scitovski and R. Scitovski and F. Martinez-Alvarez},

doi = {10.1007/s12145-017-0295-5},

issn = {1865-0481},

year  = {2017},

date = {2017-01-01},

journal = {Earth Science Informatics},

volume = {3},

number = {10},

pages = {303-320},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{IGPL2017,

title = {Using principal component analysis to improve earthquake magnitude prediction in Japan},

author = {G. Asencio-Cortés and F. Martínez-Álvarez and A. Morales-Esteban and J. Reyes and A. Troncoso},

url = {https://academic.oup.com/jigpal/article/25/6/949/4565822},

doi = {https://doi.org/10.1093/jigpal/jzx049},

year  = {2017},

date = {2017-01-01},

journal = {Logical Journal of the IGPL},

volume = {25},

number = {6},

pages = {949-966},

abstract = {Increasing attention has been paid to the prediction of earthquakes with data mining techniques during the last decade. Several works have already proposed the use of certain features serving as inputs for supervised classifiers. However, they have been successfully used without any further transformation so far. In this work, the use of principal component analysis (PCA) to reduce data dimensionality and generate new datasets is proposed. In particular, this step is inserted in a successfully already used methodology to predict earthquakes. Tokyo, one of the cities mostly threatened by large earthquakes occurrence in Japan, is studied. Several well-known classifiers combined with PCA have been used. Noticeable improvement in the results is reported.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{Asim2017,

title = {Seismic activity prediction using computational intelligence techniques in northern Pakistan},

author = {K. Asim and M. Awais and F. Martínez-Álvarez and T. Iqbal},

url = {https://link.springer.com/article/10.1007/s11600-017-0082-1},

doi = {10.1007/s11600-017-0082-1},

year  = {2017},

date = {2017-01-01},

journal = {Acta Geophysica},

volume = {65},

number = {5},

pages = {919-930},

abstract = {Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{REYES2016108,

title = {Comparison between Utsu's and Vere-Jones' aftershocks model by means of a computer simulation based on the acceptance-rejection sampling of von Neumann},

author = {J. Reyes and A. Morales-Esteban and E. González and F. Martínez-Álvarez},

url = {http://www.sciencedirect.com/science/article/pii/S0040195116302098},

doi = {10.1016/j.tecto.2016.06.005},

year  = {2016},

date = {2016-01-01},

journal = {Tectonophysics},

volume = {682},

pages = {108-119},

abstract = {In this research, a new algorithm for generating a stochastic earthquake catalog is presented. The algorithm is based on the acceptance–rejection sampling of von Neumann. The result is a computer simulation of earthquakes based on the calculated statistical properties of each zone. Vere-Jones states that an earthquake sequence can be modeled as a series of random events. This is the model used in the proposed simulation. Contrariwise, Utsu indicates that the mainshocks are special geophysical events. The algorithm has been applied to zones of Chile, China, Spain, Japan, and the USA. This allows classifying the zones according to Vere-Jones' or Utsu's model. The results have been quantified relating the mainshock with the largest aftershock within the next 5days (which has been named as Bath event). The results show that some zones fit Utsu's model and others Vere-Jones'. Finally, the fraction of seismic events that satisfy certain properties of magnitude and occurrence is analyzed.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{Asencio-Cortes2016,

title = {A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction},

author = {G. Asencio-Cortes and F. Martinez-Alvarez and A. Morales-Esteban and J. Reyes},

doi = {10.1016/j.knosys.2016.02.014},

issn = {0950-7051},

year  = {2016},

date = {2016-01-01},

journal = {Knowledge-Based Systems},

number = {101},

pages = {15-30},

abstract = {The use of different seismicity indicators as input for systems to predict earthquakes is becoming increasingly popular. Nevertheless, the values of these indicators have not been systematically obtained so far. This is mainly due to the gap of knowledge existing between seismologists and data mining experts. In this work, the effect of using different parameterizations for inputs in supervised learning algorithms has been thoroughly analyzed by means of a new methodology. Five different analyses have been conducted, mainly related to the shape of training and test sets, to the calculation of the b-value, and to the adjustment of most collected indicators. Outputs sensitivity has been determined when any of these factors is not properly taken into consideration. The methodology has been applied to four Chilean zones. Given its general-purpose design, it can be extended to any location. Similar conclusions have been drawn for all the cases: a proper selection of the sets length and a careful parameterization of certain indicators leads to significantly better results, in terms of prediction accuracy.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Florido2016,

title = {Earthquake magnitude prediction based on artificial neural networks: A survey},

author = {E. Florido and J. L. Aznarte-Mellado and A. Morales-Esteban and F. Martínez-Álvarez},

url = {https://hrcak.srce.hr/ojs/index.php/crorr/article/view/4724},

year  = {2016},

date = {2016-01-01},

journal = {Croatian Operational Research Review},

volume = {7},

number = {2},

pages = {159-169},

abstract = {The occurrence of earthquakes has been studied from many aspects. Apparently, earthquakes occur without warning and can devastate entire cities in just a few seconds, causing numerous casualties and huge economic loss. Great effort has been directed towards being able to predict these natural disasters, and taking precautionary measures. However, simultaneously predicting when, where and the magnitude of the next earthquake, within a limited region and time, seems an almost impossible task. Techniques from the field of data mining are providing new and important information to researchers. This article reviews the use of artificial neural networks for earthquake prediction in response to the increasing amount of recently published works and presenting claims of being effective. Based on an analysis and discussion of recent results, data mining practitioners are encouraged to apply their own techniques in this emerging field of research.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{FLORIDO2015112,

title = {Detecting precursory patterns to enhance earthquake prediction in Chile},

author = {E. Florido and F. Martínez-Álvarez and A. Morales-Esteban and J. Reyes and J. L. Aznarte},

url = {http://www.sciencedirect.com/science/article/pii/S0098300414002805},

doi = {10.1016/j.cageo.2014.12.002},

year  = {2015},

date = {2015-01-01},

journal = {Computers & Geosciences},

volume = {76},

pages = {112-120},

abstract = {The prediction of earthquakes is a task of utmost difficulty that has been widely addressed by using many different strategies, with no particular good results thus far. Seismic time series of the four most active Chilean zones, the country with largest seismic activity, are analyzed in this study in order to discover precursory patterns for large earthquakes. First, raw data are transformed by removing aftershocks and foreshocks, since the goal is to only predict main shocks. New attributes, based on the well-known b-value, are also generated. Later, these data are labeled, and consequently discretized, by the application of a clustering algorithm, following the suggestions found in recent literature. Earthquakes with magnitude larger than 4.4 are identified in the time series. Finally, the sequence of labels acting as precursory patterns for such earthquakes are searched for within the datasets. Results verging on 70% on average are reported, leading to conclude that the methodology proposed is suitable to be applied in other zones with similar seismicity.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MORALESESTEBAN201528,

title = {Stability analysis of a slope subject to real accelerograms by finite elements. Application to San Pedro cliff at the Alhambra in Granada},

author = {A. Morales-Esteban and J. L. de Justo and J. Reyes and J. M. Azañón and J. M. Durand and F. Martínez-Álvarez},

url = {http://www.sciencedirect.com/science/article/pii/S0267726114002255},

doi = {10.1016/j.soildyn.2014.10.023},

year  = {2015},

date = {2015-01-01},

journal = {Soil Dynamics and Earthquake Engineering},

volume = {69},

pages = {28-45},

abstract = {The dynamic stability analysis of slopes is often conducted by the traditional method of slices, using pseudo-static calculations. However, the response of a geotechnical structure subjected to seismic loads can be studied through a dynamic finite element analysis, which can be considered one of the most complete available tools, as information about the stress distribution and the deformations can be obtained. The dynamic analysis of the stability of San Pedro cliff at the Alhambra in Granada is studied in this paper. The results have been compared with pseudo-static calculations worked out with the method of slices. Real accelerograms have been selected for the dynamic tests. Thorough in situ and laboratory tests have been conducted in order to properly characterize the cliff. The soil constitutive model is also explained in this paper. Finally, the influence of the sources of energy dissipation has been studied through the material damping, the integration scheme and the boundary conditions.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{martinez2015,

title = {A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula},

author = {F. Martínez-Álvarez and D. Gutiérrez-Avilés and A. Morales-Esteban and J. Reyes and J. L. Amaro-Mellado and C. Rubio-Escudero},

url = {https://www.mdpi.com/1099-4300/17/7/5000},

doi = {10.3390/e17075000},

year  = {2015},

date = {2015-01-01},

journal = {Entropy},

volume = {17},

number = {7},

pages = {5000-5021},

abstract = {A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the availabl seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen’s self-organizing maps.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MORALESESTEBAN2014132,

title = {A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning},

author = {A. Morales-Esteban and F. Martínez-Álvarez and S. Scitovski and R. Scitovski},

url = {http://www.sciencedirect.com/science/article/pii/S0098300414002143},

doi = {10.1016/j.cageo.2014.09.003},

year  = {2014},

date = {2014-01-01},

journal = {Computers & Geosciences},

volume = {73},

pages = {132-141},

abstract = {In this paper we construct an efficient adaptive Mahalanobis k-means algorithm. In addition, we propose a new efficient algorithm to search for a globally optimal partition obtained by using the adoptive Mahalanobis distance-like function. The algorithm is a generalization of the previously proposed incremental algorithm (Scitovski and Scitovski, 2013). It successively finds optimal partitions with clusters. Therefore, it can also be used for the estimation of the most appropriate number of clusters in a partition by using various validity indexes. The algorithm has been applied to the seismic catalogues of Croatia and the Iberian Peninsula. Both regions are characterized by a moderate seismic activity. One of the main advantages of the algorithm is its ability to discover not only circular but also elliptical shapes, whose geometry fits the faults better. Three seismogenic zonings are proposed for Croatia and two for the Iberian Peninsula and adjacent areas, according to the clusters discovered by the algorithm.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MORALESESTEBAN2013121,

title = {Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence},

author = {A. Morales-Esteban and F. Martínez-Álvarez and J. Reyes},

url = {http://www.sciencedirect.com/science/article/pii/S0040195113001467},

doi = {10.1016/j.tecto.2013.02.036},

year  = {2013},

date = {2013-01-01},

journal = {Tectonophysics},

volume = {593},

pages = {121-134},

abstract = {A method to predict earthquakes in two of the seismogenic areas of the Iberian Peninsula, based on Artificial Neural Networks (ANNs), is presented in this paper. ANNs have been widely used in many fields but only very few and very recent studies have been conducted on earthquake prediction. Two kinds of predictions are provided in this study: a) the probability of an earthquake, of magnitude equal or larger than a preset threshold magnitude, within the next 7 days, to happen; b) the probability of an earthquake of a limited magnitude interval to happen, during the next 7 days. First, the physical fundamentals related to earthquake occurrence are explained. Second, the mathematical model underlying ANNs is explained and the configuration chosen is justified. Then, the ANNs have been trained in both areas: The Alborán Sea and the Western Azores–Gibraltar fault. Later, the ANNs have been tested in both areas for a period of time immediately subsequent to the training period. Statistical tests are provided showing meaningful results. Finally, ANNs were compared to other well known classifiers showing quantitatively and qualitatively better results. The authors expect that the results obtained will encourage researchers to conduct further research on this topic.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{REYES20131314,

title = {Neural networks to predict earthquakes in Chile},

author = {J. Reyes and A. Morales-Esteban and F. Martínez-Álvarez},

url = {http://www.sciencedirect.com/science/article/pii/S1568494612004656},

doi = {10.1016/j.asoc.2012.10.014},

year  = {2013},

date = {2013-01-01},

journal = {Applied Soft Computing},

volume = {13},

number = {2},

pages = {1314-1328},

abstract = {A new earthquake prediction system is presented in this work. This method, based on the application of artificial neural networks, has been used to predict earthquakes in Chile, one of the countries with larger seismic activity. The input values are related to the b-value, the Bath's law, and the Omori–Utsu's law, parameters that are strongly correlated with seismicity, as shown in solid previous works. Two kind of prediction are provided in this study: The probability that an earthquake of magnitude larger than a threshold value happens, and the probability that an earthquake of a limited magnitude interval might occur, both during the next five days in the areas analyzed. For the four Chile's seismic regions examined, with epicenters placed on meshes with dimensions varying from 0.5° × 0.5° to 1° × 1°, a prototype of neuronal network is presented. The prototypes predict an earthquake every time the probability of an earthquake of magnitude larger than a threshold is sufficiently high. The threshold values have been adjusted with the aim of obtaining as few false positives as possible. The accuracy of the method has been assessed in retrospective experiments by means of statistical tests and compared with well-known machine learning classifiers. The high success rate achieved supports the suitability of applying soft computing in this field and poses new challenges to be addressed.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZALVAREZ2013198,

title = {Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula},

author = {F. Martínez-Álvarez and J. Reyes and A. Morales-Esteban and C. Rubio-Escudero},

url = {http://www.sciencedirect.com/science/article/pii/S0950705113001871},

doi = {10.1016/j.knosys.2013.06.011},

year  = {2013},

date = {2013-01-01},

journal = {Knowledge-Based Systems},

volume = {50},

pages = {198-210},

abstract = {This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the information gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been characterized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MORALESESTEBAN2012174,

title = {Probabilistic method to select calculation accelerograms based on uniform seismic hazard acceleration response spectra},

author = {A. Morales-Esteban and J. L. de Justo and F. Martínez-Álvarez and J. M. Azañón},

url = {http://www.sciencedirect.com/science/article/pii/S0267726112001601},

doi = {10.1016/j.soildyn.2012.07.003},

year  = {2012},

date = {2012-01-01},

journal = {Soil Dynamics and Earthquake Engineering},

volume = {43},

number = {3},

pages = {174-185},

abstract = {A dynamic analysis of a structure requires the previous definition of the accelerograms and the structure characteristics. The response of a structure subject to a seismic movement can be determined by two methods: either using the accelerograms recorded near the site, or using visco-elastic response spectra. The first method should only be used for locations where many accelerograms have been recorded, and needs a probabilistic calculation to ascertain the design accelerograms. The use of visco-elastic response spectra is based upon the fact that the response spectrum is the soil movement parameter better related to the structural response and is more adequate to obtain accelerograms in regions where the number of records is insufficient. This is the most commonly used method as the response of structures, in the elastic linear range, can be obtained as the superposition of a few modes of vibration. A probabilistic method for selecting calculation accelerograms is presented in this paper. First, the probabilistic hazard equation is solved. Based on the hazard curves obtained, the uniform seismic hazard acceleration response spectrum (USHARS) is constructed for the location, according to the type of soil and the required hazard level (exposure time and exceedance probability). Then, calculation accelerograms are selected. Based on this methodology, real accelerograms, for a return period of 975 years, have been obtained for San Pedro Cliff (Spain) at the Alhambra in Granada.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{ESWA2010,

title = {Pattern Recognition to Forecast Seismic Time Series},

author = {A. Morales-Esteban and F. Martínez-Álvarez and A. Troncoso and J. L. Justo and C. Rubio-Escudero},

url = {https://www.sciencedirect.com/science/article/pii/S0957417410004616},

doi = {10.1016/j.eswa.2010.05.050},

year  = {2010},

date = {2010-01-01},

journal = {Expert System with Applications},

volume = {37},

pages = {8333-8342},

abstract = {Earthquakes arrive without previous warning and can destroy a whole city in a few seconds, causing numerous deaths and economical losses. Nowadays, a great effort is being made to develop techniques that forecast these unpredictable natural disasters in order to take precautionary measures. In this paper, clustering techniques are used to obtain patterns which model the behavior of seismic temporal data and can help to predict medium–large earthquakes. First, earthquakes are classified into different groups and the optimal number of groups, a priori unknown, is determined. Then, patterns are discovered when medium–large earthquakes happen. Results from the Spanish seismic temporal data provided by the Spanish Geographical Institute and non-parametric statistical tests are presented and discussed, showing a remarkable performance and the significance of the obtained results.},

keywords = {natural disasters},

pubstate = {published},

tppubtype = {article}

}