Publications

@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{Gomez-Losada2018b,

title = {A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information},

author = {A. Gomez-Losada and G. Asencio-Cortes and F. Martinez-Alvarez and J. C. Riquelme},

doi = {10.1016/j.envsoft.2018.08.013},

issn = {1364-8152},

year  = {2018},

date = {2018-01-01},

journal = {Environmental Modelling and Software},

number = {110},

pages = {52-61},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{BOKDE201888,

title = {A novel imputation methodology for time series based on pattern sequence forecasting},

author = {N. Bokde and Marcus W. Beck and F. Martínez-Álvarez and K. Kulat},

url = {http://www.sciencedirect.com/science/article/pii/S0167865518306500},

doi = {10.1016/j.patrec.2018.09.020},

year  = {2018},

date = {2018-01-01},

journal = {Pattern Recognition Letters},

volume = {116},

pages = {88-96},

abstract = {The Pattern Sequence Forecasting (PSF) algorithm is a previously described algorithm that identifies patterns in time series data and forecasts values using periodic characteristics of the observations. A new method for univariate time series is introduced that modifies the PSF algorithm to simultaneously forecast and backcast missing values for imputation. The imputePSF method extends PSF by characterizing repeating patterns of existing observations to provide a more precise estimate of missing values compared to more conventional methods, such as replacement with means or last observation carried forward. The imputation accuracy of imputePSF was evaluated by simulating varying amounts of missing observations with three univariate datasets. Comparisons of imputePSF with well-established methods using the same simulations demonstrated an overall reduction in error estimates. The imputePSF algorithm can produce more precise imputations on appropriate datasets, particularly those with periodic and repeating patterns.},

keywords = {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{PlosOne2018,

title = {Earthquake prediction model using support vector regressor and hybrid neural networks},

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

url = {https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0199004},

doi = {10.1371/journal.pone.0199004},

year  = {2018},

date = {2018-01-01},

journal = {PLOS ONE},

volume = {13},

number = {7},

pages = {1-22},

abstract = {Earthquake prediction has been a challenging research area, where a future occurrence of the devastating catastrophe is predicted. In this work, sixty seismic features are computed through employing seismological concepts, such as Gutenberg-Richter law, seismic rate changes, foreshock frequency, seismic energy release, total recurrence time. Further, Maximum Relevance and Minimum Redundancy (mRMR) criteria is applied to extract the relevant features. A Support Vector Regressor (SVR) and Hybrid Neural Network (HNN) based classification system is built to obtain the earthquake predictions. HNN is a step wise combination of three different Neural Networks, supported by Enhanced Particle Swarm Optimization (EPSO), to offer weight optimization at each layer. The newly computed seismic features in combination with SVR-HNN prediction system is applied on Hindukush, Chile and Southern California regions. The obtained numerical results show improved prediction performance for all the considered regions, compared to previous prediction studies.},

keywords = {},

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-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{Energy2018,

title = {Stacking ensemble learning for short-term electricity consumption forecasting},

author = {F. Divina and A. Gilson and F. Goméz-Vela and M. García-Torres and J. F. Torres},

url = {https://www.mdpi.com/1996-1073/11/4/949},

doi = {https://doi.org/10.3390/en11040949},

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {4},

pages = {949},

abstract = {The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Gutierrez-Aviles2018,

title = {TRIQ: a new method to evaluate triclusters},

author = {D. Gutiérrez-Avilés and R. Giráldez and F. J. Gil-Cumbreras and C. Rubio-Escudero},

url = {https://biodatamining.biomedcentral.com/articles/10.1186/s13040-018-0177-5},

doi = {10.1186/s13040-018-0177-5},

year  = {2018},

date = {2018-01-01},

journal = {BioData Mining},

volume = {11},

number = {1},

pages = {15},

abstract = {Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO).},

keywords = {bioinformatics, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ENV2018,

title = {Imbalanced classification techniques for monsoon forecasting based on a new climatic time series},

author = {A. Troncoso and P. Ribera and G. Asencio-Cortés and I. Vega and D. Gallego},

url = {https://www.sciencedirect.com/science/article/pii/S1364815217301950},

doi = {10.1016/j.envsoft.2017.11.024},

year  = {2018},

date = {2018-01-01},

journal = {Environmental Modelling & Software},

volume = {106},

number = {6},

pages = {48-56},

abstract = {Monsoons have been widely studied in the literature due to their climatic impact related to precipitation 

and temperature over different regions around the world. In this work, data mining techniques, namely 

imbalanced classification techniques, are proposed in order to check the capability of climate indices to 

capture and forecast the evolution of theWestern North Pacific Summer Monsoon. Thus, the main goal is 

to predict if the monsoon will be an extreme monsoon for a temporal horizon of a month. Firstly, a new 

monthly index of the monsoon related to its intensity has been generated. Later, the problem of forecasting 

has been transformed into a binary imbalanced classification problem and a set of representative 

techniques, such as models based on trees, models based on rules, black box models and ensemble 

techniques, are applied to obtain the forecasts. From the results obtained, it can be concluded that the 

methodology proposed here reports promising results according to the quality measures evaluated and 

predicts extreme monsoons for a temporal horizon of a month with a high accuracy.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2018,

title = {Big data analytics for discovering electricity consumption patterns in smart cities},

author = {R. Pérez-Chacón and J. M. Luna and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme},

url = {http://www.mdpi.com/1996-1073/11/3/683

},

doi = {10.3390/en11030683 },

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {3},

pages = {683},

abstract = {New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{BIODM2018,

title = {Pairwise gene GO-based measures for biclustering of high-dimensional expression data},

author = {J. A. Nepomuceno and A. Troncoso and J. S. Aguilar-Ruiz},

url = {https://www.ncbi.nlm.nih.gov/pubmed/29610579},

doi = {10.1186/s13040-018-0165-9},

year  = {2018},

date = {2018-01-01},

journal = {BioData Mining},

volume = {11},

number = {4},

abstract = {BACKGROUND: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. RESULTS: The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. CONCLUSIONS: It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.},

keywords = {bioinformatics},

pubstate = {published},

tppubtype = {article}

}

@article{INFSCI2018,

title = {A novel Spark-based multi-step forecasting algorithm for big data time series},

author = {A. Galicia and J. F. Torres and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025518304493},

doi = {10.1016/j.ins.2018.06.010},

year  = {2018},

date = {2018-01-01},

journal = {Information Sciences},

volume = {467},

pages = {800-818},

abstract = {This paper presents different scalable methods for predicting big time series, namely time series with a high frequency measurement. Methods are also developed to deal with arbitrary prediction horizons. The Apache Spark framework is proposed for distributed computing in order to achieve the scalability of the methods. Prediction methods have been developed using Spark’s MLlib library for machine learning. Since the library does not support multivariate regression, the prediction problem is formulated as h prediction sub-problems, where h is the number of future values to predict, that is, the prediction horizon. Furthermore, different kinds of representative methods have been chosen, such as decision trees, two tree-based ensemble techniques (Gradient-Boosted and Random Forest) and a linear regression method as a reference method for comparisons. Finally, the methodology has been tested in a real time series of electrical demand in Spain, with a time interval of ten minutes between measurements.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{KNOSYS2018b,

title = {Big data time series forecasting based on nearest neighbors distributed computing with Spark},

author = {R. Talavera-Llames and R. Pérez-Chacón and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0950705118303770},

doi = {10.1016/j.knosys.2018.07.026},

year  = {2018},

date = {2018-01-01},

journal = {Knowledge-Based Systems},

volume = {161},

number = {1},

pages = {12-25},

abstract = {A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm is introduced in this work. Such an algorithm has been developed for distributed computing under the Apache Spark framework. Every phase of the algorithm is explained in this work, along with how the optimal values of the input parameters required for the algorithm are obtained. In order to test the developed algorithm, a Spanish energy consumption big data time series has been used. The accuracy of the prediction has been assessed showing remarkable results. Additionally, the optimal configuration of a Spark cluster has been discussed. Finally, a scalability analysis of the algorithm has been conducted leading to the conclusion that the proposed algorithm is highly suitable for big data environments.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Martinez18,

title = {Data Science and Big Data in Energy Forecasting},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},

doi = {10.3390/en11113224},

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {11},

pages = {3224},

keywords = {big data, energy},

pubstate = {published},

tppubtype = {article}

}

@article{ICAE2018,

title = {A scalable approach based on deep learning for big data time series forecasting},

author = {J. F. Torres and A. Galicia and A. Troncoso and F. Martínez-Álvarez},

url = {https://content.iospress.com/articles/integrated-computer-aided-engineering/ica580},

doi = {10.3233/ICA-180580},

year  = {2018},

date = {2018-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {25},

number = {4},

pages = {335-348},

abstract = {This paper presents a method based on deep learning to deal with big data times series forecasting. The deep feed forward neural network provided by the H2O big data analysis framework has been used along with the Apache Spark platform for distributed computing. Since H2O does not allow the conduction of multi-step regression, a general-purpose methodology that can be used for prediction horizons with arbitrary length is proposed here, being the prediction horizon, h, the number of future values to be predicted. The solution consists in splitting the problem into h forecasting subproblems, being h the number of samples to be simultaneously predicted. Thus, the best prediction model for each subproblem can be obtained, making easier its parallelization and adaptation to the big data context. Moreover, a grid search is carried out to obtain the optimal hyperparameters of the deep learning-based approach. Results from a real-world dataset composed of electricity consumption in Spain, with a ten-minute frequency sampling rate, from 2007 to 2016 are reported. In particular, the accuracy and runtimes versus computing resources and size of the dataset are analyzed. Finally, the performance and the scalability of the proposed method is compared to other recently published techniques, showing to be a suitable method to process big data time series.},

keywords = {deep learning, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{PO:EP-2018,

title = {The Blessing of Dimensionality: Feature Selection Outperforms Functional Connectivity-based Feature Transformation to Classify ADHD Subjects from EEG Patterns of Phase Synchronisation},

author = {E. Pereda and M. García-Torres and B. Melián and S. Ma~nas and L. Méndez and J. González},

url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201660},

doi = {10.1371/journal.pone.0201660},

year  = {2018},

date = {2018-01-01},

journal = {PLoS ONE},

volume = {13},

number = {8},

keywords = {bioinformatics},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018a,

title = {Gaia Data Release 2. Observational Hertzsprung-Russell diagrams},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/full_html/2018/08/aa32843-18/aa32843-18.html},

doi = {10.1051/0004-6361/201832843},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A10},

keywords = {astrostatistics},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018b,

title = {Gaia Data Release 2. Summary of the contents and survey properties},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/full_html/2018/08/aa33051-18/aa33051-18.html},

doi = {10.1051/0004-6361/201833051},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A1},

keywords = {astrostatistics},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018c,

title = {Gaia Data Release 2. Observations of Solar System Objects},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/abs/2018/08/aa32900-18/aa32900-18.html},

doi = {10.1051/0004-6361/201832900},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A13},

keywords = {astrostatistics},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018d,

title = {Gaia Data Release 2. Mapping the milky way disc kinematics},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/abs/2018/08/aa32865-18/aa32865-18.html},

doi = {10.1051/0004-6361/201832865},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A11},

keywords = {astrostatistics},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018e,

title = {Gaia Data Release 2. The celestial reference frame (Gaia-CRF2)},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/full_html/2018/08/aa32916-18/aa32916-18.html},

doi = {10.1051/0004-6361/201832916},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A14},

keywords = {astrostatistics},

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{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{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{KNOSYS2017,

title = {Content-based methods in peer assessment of open-response questions to grade students as authors and as graders},

author = {O. Luaces and J. Díez and A. Alonso-Betanzos and A. Troncoso and A. Bahamonde},

url = {http://www.sciencedirect.com/science/article/pii/S0950705116301964},

doi = {10.1016/j.knosys.2016.06.024},

year  = {2017},

date = {2017-01-01},

journal = {Knowledge-Based Systems},

volume = {117},

pages = {79-87},

abstract = {Massive Open Online Courses (MOOCs) use different types of assignments in order to evaluate student knowledge. Multiple-choice tests are particularly apt given the possibility for automatic assessment of large numbers of assignments. However, certain skills require open responses that cannot be assessed automatically yet their evaluation by instructors or teaching assistants is unfeasible given the large number of students. A potentially effective solution is peer assessment whereby students grade the answers of other students. However, to avoid bias due to inexperience, such grades must be filtered. We describe a factorization approach to grading, as a scalable method capable of dealing with very high volumes of data. Our method is also capable of representing open-response content using a vector space model of the answers. Since reliable peer assessment requires students to make coherent assessments, students can be motivated by their assessments reflecting not only their own answers but also their efforts as graders. The method described is able to tackle both these aspects simultaneously. Finally, for a real-world university setting in Spain, we compared grades obtained by our method and grades awarded by university instructors, with results indicating a notable improvement from using a content-based approach. There was no evidence that instructor grading would have led to more accurate grading outcomes than the assessment produced by our models.},

keywords = {big data},

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{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{Energies2017,

title = {Recent Advances in energy Time Series Forecasting},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},

url = {http://www.mdpi.com/1996-1073/10/6/809},

doi = {10.3390/en10060809},

year  = {2017},

date = {2017-01-01},

journal = {Energies},

volume = {10},

number = {6},

pages = {809},

abstract = {This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{CIN2017,

title = {Applications of computational intelligence in Time Series},

author = {F. Martínez-Álvarez and A. Troncoso and J. Reyes and M. Martínez-Ballesteros and J. C. Riquelme},

url = {https://www.hindawi.com/journals/cin/si/467684/},

doi = {10.1155/2017/9361749},

year  = {2017},

date = {2017-01-01},

journal = {Computational Intelligence and Neuroscience},

volume = {article id 9361749},

keywords = {time series},

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{AA:Gaia-2017a,

title = {Gaia Data Release 1. Open cluster astrometry: performance, limitations, and future prospects},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/abs/2017/05/aa30552-17/aa30552-17.html},

doi = {10.1051/0004-6361/201730552},

year  = {2017},

date = {2017-01-01},

journal = {Astronomy & Astrophysics},

volume = {601},

number = {A19},

keywords = {astrostatistics},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2017b,

title = {Gaia Data Release 1. Testing the parallaxes with local Cepheids and RR Lyrae stars},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/abs/2017/09/aa29925-16/aa29925-16.html},

doi = {10.1051/0004-6361/201629925},

year  = {2017},

date = {2017-01-01},

journal = {Astronomy & Astrophysics},

volume = {605},

number = {A79},

keywords = {astrostatistics},

pubstate = {published},

tppubtype = {article}

}

@article{Bokde2016a,

title = {PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm},

author = {N. Bokde and G. Asencio-Cortes and F. Martinez-Alvarez and K. Kulat},

issn = {2073-4859},

year  = {2017},

date = {2017-01-01},

journal = {R Journal},

volume = {1},

number = {9},

pages = {324-333},

abstract = {This paper discusses about an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its implementation with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example of usage. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository.},

keywords = {time series},

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{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{MARTINEZ16,

title = {Improving a multi-objective evolutionary algorithm to discover quantitative association rules},

author = {M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme},

url = {https://link.springer.com/article/10.1007/s10115-015-0911-y},

doi = {https://doi.org/10.1007/s10115-015-0911-y},

year  = {2016},

date = {2016-11-01},

journal = {Knowledge and Information Systems},

volume = {49},

pages = {481-509},

keywords = {association rules},

pubstate = {published},

tppubtype = {article}

}

@article{ASENCIO16,

title = {A novel methodology to predict urban traffic congestion with ensemble learning},

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

url = {https://link.springer.com/article/10.1007/s00500-016-2288-6},

doi = {https://doi.org/10.1007/s00500-016-2288-6},

year  = {2016},

date = {2016-11-01},

journal = {Knowledge and Information Systems},

volume = {20},

pages = {4205–4216},

keywords = {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}

}