Publications

@article{RING2025_Martin,

title = {Metalearning for improving time series forecasting based on deep learning: A water case study},

author = {Martín Solís and A. Gil-Gamboa and Alicia Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S2590123025035960},

doi = {10.1016/j.rineng.2025.107541},

year  = {2025},

date = {2025-12-09},

urldate = {2025-12-09},

journal = {Results in Engineering},

volume = {28},

pages = {107541},

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

pubstate = {published},

tppubtype = {article}

}

@article{INFFUSChacon2025,

title = { A multimodal hybrid deep learning approach for pest forecasting using time series and satellite images},

author = {A. M. Chacón-Maldonado and G. Asencio-Cortés and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525004233},

doi = {10.1016/j.inffus.2025.103350},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {Information Fusion},

volume = {124},

pages = {103350},

keywords = {deep learning, precision agriculture},

pubstate = {published},

tppubtype = {article}

}

@article{ULLAH25,

title = {A Novel Explainable AI Framework for Medical Image Classification Integrating Statistical, Visual, and Rule-Based Methods},

author = {N. Ullah and F. Guzmán-Aroca and F. Martínez-Álvarez and I. De Falco and G. Sannino},

url = {https://www.sciencedirect.com/science/article/pii/S1361841525002129},

doi = {https://doi.org/10.1016/j.media.2025.103665},

year  = {2025},

date = {2025-06-06},

urldate = {2025-06-06},

journal = {Medical Image Analysis},

volume = {105},

pages = {103665},

abstract = {Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in highstakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability’s depth and clarity. This work proposes a novel explainable AI method specifically designed for medical image analysis, integrating statistical, visual, and rule-based explanations to improve transparency in deep learning models. Statistical features are derived from deep features extracted using a custom Mobilenetv2 model. A two-step feature selection method—zero-based filtering with mutual importance selection—ranks and refines these features. Decision tree and RuleFit models

are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets—COVID-19 radiography, ultrasound

breast cancer, brain tumour magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images—with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.},

keywords = {association rules, deep learning, feature selection, XAI},

pubstate = {published},

tppubtype = {article}

}

@article{GIL-GAMBOA25,

title = {Energy-efficient transfer learning for water consumption forecasting},

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

url = {https://www.sciencedirect.com/science/article/pii/S2210537925000502},

doi = {https://doi.org/10.1016/j.suscom.2025.101130},

year  = {2025},

date = {2025-05-07},

urldate = {2025-05-07},

journal = {Sustainable Computing: Informatics and Systems},

volume = {46},

pages = {101130},

abstract = {Artificial intelligence is expanding at an unprecedented rate due to the numerous advantages it provides to all types of businesses and industries. Water utilities are adopting artificial intelligence models to optimize water management in cities nowadays. However, the substantial computational demands of artificial intelligence present challenges, particularly regarding energy consumption and environmental impact. This paper addresses this problem by proposing a transfer learning approach for water consumption forecasting that reduces computational time, energy usage, and CO$_2$ emissions. The proposed methodology consists in developing a transfer learning approach based on a deep learning model already trained for a task with similar characteristics such as predicting electricity consumption. Thus, a pre-trained deep learning model designed for electricity consumption prediction is adapted to the water consumption domain, leveraging shared characteristics between these tasks. Experiments are conducted to determine the optimal amount of knowledge transfer and compare the performance of this approach with other state-of-the-art time-series forecasting models. Using real data from a water company in Spain, the transfer learning model achieves a similar or better accuracy than the other methods, while demonstrating significantly lower computational times, energy consumption and CO2 emissions. In addition, a scalability analysis has been conducted leading to the conclusion that the proposed transfer learning model is highly suitable to deal with big data. These findings highlight the potential of transfer learning as a sustainable and scalable solution for big data challenges in water management systems.},

keywords = {deep learning, forecasting, time series, transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{GUTIERREZ-AVILES25,

title = {MetaGen: A framework for metaheuristic development and hyperparameter optimization in machine and deep learning},

author = {D. Gutiérrez-Avilés and M. J. Jiménez-Navarro and J. F. Torres and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0925231225007180},

doi = {https://doi.org/10.1016/j.neucom.2025.130046},

year  = {2025},

date = {2025-03-15},

urldate = {2025-03-15},

journal = {Neurocomputing},

volume = {637},

pages = {130046},

abstract = {Hyperparameter optimization is a pivotal step in enhancing model performance within machine learning. Traditionally, this challenge is addressed through metaheuristics, which efficiently explore large search spaces to uncover optimal solutions. However, implementing these techniques can be complex without adequate development tools, which is the primary focus of this paper. Hence, we introduce MetaGen, a novel Python package designed to provide a comprehensive framework for developing and evaluating metaheuristic algorithms. MetaGen follows best practices in Python design, ensuring minimalistic code implementation, intuitive comprehension, and full flexibility in solution representation. The package defines two distinct user roles: Developers, responsible for algorithm implementation for hyperparameter optimization, and Solvers, who leverage pre-implemented metaheuristics to address optimization problems. Beyond algorithm implementation, MetaGen facilitates benchmarking through built-in test functions, ensuring standardized performance comparisons. It also provides automated reporting and visualization tools to analyze optimization progress and outcomes effectively. Furthermore, its modular design allows distribution

and integration into existing machine learning workflows. Several illustrative use cases are presented to demonstrate its adaptability and efficacy. The package, along with code, a user manual, and supplementary materials, is available at: https://github.com/Data-Science-Big-Data-Research-Lab/MetaGen.},

keywords = {big data, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{HABTEMARIAM25,

title = {A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia},

author = {E. T. Habtemariam and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S092523122500699X},

doi = {https://doi.org/10.1016/j.neucom.2025.130027},

year  = {2025},

date = {2025-03-13},

urldate = {2025-03-13},

journal = {Neurocomputing},

volume = {637},

pages = {130027},

abstract = {Predicting energy consumption accurately is crucial for optimizing energy management strategies and achieving sustainability goals. Traditional methods often struggle with the complexity of energy consumption patterns, particularly in developing regions such as Ethiopia, where unique challenges exist. This study proposes an ensemble deep learning approach that integrates multiple models to enhance prediction accuracy.

Additionally, as a previous step, a clustering process has been applied to discover different groups of customers. Our method combines deep learning architectures, including Gated Recurrent Units, Long Short-Term Memory, and Convolutional Neural Networks, within an optimized ensemble with weights computed with the Coronavirus Optimization Algorithm. This approach aims to leverage the strengths of each model

to produce robust and reliable predictions. We demonstrate that our ensemble approach yields competitive results, outperforming individual models within the ensemble. By integrating diverse models, our framework captures nuanced patterns in energy consumption data more effectively, contributing to improved prediction accuracy. Furthermore, we validate the effectiveness of our approach using three distinct datasets from Ethiopia for three different customer clusters. These datasets represent different regions and consumption profiles within the country, ensuring the robustness and generalizability of our proposed methodology.},

keywords = {clustering, deep learning, energy, forecasting},

pubstate = {published},

tppubtype = {article}

}

@article{CASAS-GOMEZ25,

title = {Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas},

author = {P. Casas-Gómez and J. F. Torres and J. C. Linares and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S157495412400493X?via%3Dihub},

doi = {https://doi.org/10.1016/j.ecoinf.2024.102951},

year  = {2025},

date = {2025-01-01},

journal = {Ecological Informatics},

volume = {85},

pages = {102951},

abstract = {This study addresses the task of forecasting Basal Area Increment trends in forest ecosystems, which is essential for conservation and biodiversity management, particularly in the context of climate change. Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. Our methodology includes rigorous hyperparameter tuning to optimize the Deep Learning models’ architecture. We evaluate the models’ performance across 15 species in the Himalayan region, individually and collectively, using temperature and precipitation data as predictors. The Deep Learning model significantly outperforms state-of-the-art techniques, achieving the lowest Root Mean Squared Error (7.407 for LSTM and 6.202 for TCNN), highest 𝑅2 (0.495 for LSTM an0.585 for TCNN) and lowest Mean Absolute Percentage Error values (37.653 for LSTM and 34.296 for TCNN). These findings highlight the potential of Deep Learning networks to provide accurate and reliable Basal AreaIncrement forecasts, offering valuable insights for forest management and conservation efforts in the face of ongoing climate change.},

keywords = {deep learning, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Chacon2025,

title = {A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting},

author = {A. M. Chacón-Maldonado and L. Melgar-García and G. Asencio-Cortés and A. Troncoso},

url = {https://link.springer.com/article/10.1007/s00521-024-10731-z},

doi = {https://doi.org/10.1007/s00521-024-10731-z},

year  = {2025},

date = {2025-01-01},

urldate = {2024-01-01},

journal = {Neural Computing and Applications},

keywords = {deep learning, precision agriculture, XAI},

pubstate = {published},

tppubtype = {article}

}

@article{vanhaeren2025application,

title = {Application of XAI to the prediction of CTCF binding sites},

author = {T. Vanhaeren and A. R. Troncoso-García and J. F. Torres and F. Divina and P. M. Martínez-García},

url = {https://www.sciencedirect.com/science/article/pii/S259012302402019X},

doi = {10.1016/j.rineng.2024.103776},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Results in Engineering},

volume = {25},

pages = {103776},

abstract = {The inherent ‘black box’ nature of deep learning models has hindered their widespread adoption in certain fields, as they provide limited transparency into the reasoning behind their predictions. In the last years, Explainable Artificial Intelligence (XAI) techniques have proven to be effective not only in prediction itself but also in the extraction of meaningful knowledge from deep learning models by means of feature interpretation. In this study, Local Interpretable Model-agnostic Explanations are applied to the prediction of CTCF binding sites, a common task in the field of genomics. Good prediction performances and inferred explanations are obtained that highlight the most informative features that contribute to predictions such as chromatin accessibility and cis-regulatory elements which align well with previously reported data. This work represents a proof of concept showing that XAI are suitable for the extraction of molecular insights from complex biological problems like CTCF binding prediction.},

keywords = {big data, bioinformatics, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{Vellinger2025Livestock,

title = {Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning},

author = {A. Vellinger and F. Rodriguez-Diaz and F. Divina and J. F. Torres},

url = {https://doi.org/10.1093/jigpal/jzaf034},

doi = {10.1093/jigpal/jzaf034},

year  = {2025},

date = {2025-01-01},

journal = {Logic Journal of the IGPL},

volume = {to appear},

number = {to appear},

pages = {to appear},

abstract = {In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming.},

keywords = {deep learning, pattern recognition, time series},

pubstate = {published},

tppubtype = {article}

}

@article{RODRIGUEZ24,

title = {Explainable Olive grove and Grapevine pest forecasting through machine learning-based classification and regression},

author = {F. Rodríguez-Díaz and A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés},

url = {https://www.sciencedirect.com/science/article/pii/S2590123024013136},

doi = {https://doi.org/10.1016/j.rineng.2024.103058},

year  = {2024},

date = {2024-09-09},

urldate = {2024-09-09},

journal = {Results in Engineering},

volume = {24},

pages = {103058},

abstract = {Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.},

keywords = {deep learning, feature selection, time series, XAI},

pubstate = {published},

tppubtype = {article}

}

@article{JIMENEZ-NAVARRO24b,

title = {Embedded feature selection for neural networks via learnable drop layer},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Brito and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://academic.oup.com/jigpal/advance-article/doi/10.1093/jigpal/jzae062/7689640},

doi = {https://doi.org/10.1093/jigpal/jzae062},

year  = {2024},

date = {2024-07-06},

urldate = {2024-07-06},

journal = {Logic Journal of the IGPL},

pages = {jzae062},

abstract = {Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains a selection of the most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results.},

keywords = {deep learning, feature selection, time series},

pubstate = {published},

tppubtype = {article}

}

@article{JIMENEZ-NAVARRO24,

title = {Explaining deep learning models for ozone pollution prediction via embedded feature selection},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://www.sciencedirect.com/science/article/pii/S1568494624002783},

doi = {https://doi.org/10.1016/j.asoc.2024.111504},

year  = {2024},

date = {2024-04-04},

journal = {Applied Soft Computing},

volume = {157},

pages = {111504},

abstract = {Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects climate warming, crop productivity, and more. Its formation occurs when nitrogen oxides and volatile organic compounds react with short-wavelength solar radiation. Consequently, urban areas with high traffic volume and elevated temperatures are particularly prone to elevated O3 levels, which pose a significant health risk to their inhabitants. In response to this problem, many countries have developed web and mobile applications that provide real-time air pollution information using sensor data. However, while these applications offer valuable insight into current pollution levels, predicting future pollutant behavior is crucial for effective planning and mitigation strategies. Therefore, our main objectives are to develop accurate and efficient prediction models and identify the key factors that influence O3 levels. We adopt a time series forecasting approach to address these objectives, which allows us to analyze and predict O3 future behavior. Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability. Our study focuses on data collected from five representative areas in Seville, Cordova, and Jaen provinces in Spain, using multiple sensors to capture comprehensive pollution data. We compare the performance of three models: Lasso, Decision Tree, and Deep Learning with and without incorporating the Time Selection Layer. Our results demonstrate that including the Time Selection Layer significantly enhances the effectiveness and interpretability of Deep Learning models, achieving an average effectiveness improvement of 9% across all monitored areas.},

keywords = {deep learning, feature selection, time series, XAI},

pubstate = {published},

tppubtype = {article}

}

@article{GARCIA-SOTO24,

title = {Water consumption time series forecasting in urban centers using deep neural networks},

author = {C. G. García-Soto and J. F. Torres and M. A. Zamora-Izquierdo and J. Palma and A. Troncoso},

url = {https://link.springer.com/article/10.1007/s13201-023-02072-4},

doi = {https://doi.org/10.1007/s13201-023-02072-4},

year  = {2024},

date = {2024-01-12},

journal = {Applied Water Science},

volume = {14},

pages = {1-14},

abstract = {The time series analysis and prediction techniques are highly valued in many application felds, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for diferent time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifcally designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.},

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

pubstate = {published},

tppubtype = {article}

}

@article{JIMENEZ-NAVARRO23a,

title = {From Simple to Complex: A Sequential Method for Enhancing Time Series Forecasting with Deep Learning},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso and G. Asencio-Cortés},

url = {https://academic.oup.com/jigpal/advance-article/doi/10.1093/jigpal/jzae030/7670726},

doi = {10.1093/jigpal/jzae030},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Logic Journal of the IGPL},

volume = {32},

number = {6},

pages = {986-1003},

abstract = {Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished.Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer (TFT), NBEATS and Echo State Network (ESN).},

keywords = {deep learning, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ESWA2024,

title = {Medium-term water consumption forecasting based on deep neural networks},

author = {A. Gil-Gamboa and P. Paneque and O. Trull and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S095741742400099X},

doi = {10.1016/j.eswa.2024.123234},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Expert Systems With Applications},

volume = {247},

pages = {123234},

abstract = {Water consumption forecasting is an essential tool for water management, as it allows for efficient planning and allocation of water resources, an undervalued but indispensable resource for all living beings. With the increasing demand for accurate and timely water forecasting, traditional forecasting methods are proving to be insufficient. Deep learning techniques, which have shown remarkable performance in a wide range of applications, offer a promising approach to address the challenges of water consumption forecasting. In this work, the use of deep learning models for medium-term water consumption forecasting of residential areas is explored. A deep feed-forward neural network is developed to predict water consumption of a company’s customers for the next quarter. First, customers are grouped according to their consumption as these customers include both household consumers and special consumers such as public swimming pools, sports halls or small industries. Then, a deep feed-forward neural network is designed for household customers by obtaining the optimal values for those hyperparameters that have a great influence on the network performance. Results are reported using a real-world dataset composed of the water consumption from 1999 to 2015 on a quarterly basis, corresponding to 3262 clients of a water supply company. Finally, the proposed algorithm is evaluated by comparing it with other reference algorithms including an LSTM network.},

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

pubstate = {published},

tppubtype = {article}

}

@article{TRONCOSO-GARCIA23b,

title = {Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting},

author = {A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0168169923007755},

doi = {https://doi.org/10.1016/j.compag.2023.108387},

year  = {2023},

date = {2023-11-08},

journal = {Computers and Electronics in Agriculture},

volume = {215},

pages = {108387},

abstract = {Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time},

keywords = {deep learning, forecasting, precision agriculture, XAI},

pubstate = {published},

tppubtype = {article}

}

@article{HADJOUT23,

title = {Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market},

author = {D. Hadjout and A. Sebaa and J. F. Torres and F. Mártinez-Álvarez},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0957417423006255},

doi = {https://doi.org/10.1016/j.eswa.2023.120123},

year  = {2023},

date = {2023-10-01},

journal = {Expert Systems with Applications},

volume = {227},

pages = {120123},

abstract = {The reduction of electricity loss and the effective management of electricity demand are vital operations for production and distribution electricity enterprises. To achieve these goals, accurate forecasts of aggregate and individual electricity consumers are necessary. A novel multistep forecasting method is developed to forecast medium-term electricity consumption of the Algerian economic sector. The proposed method goes through the following three steps: cleaning steps, clustering steps and forecasting step of each cluster. The aim of the first step is to detect and then replace outliers. To complete the first phase, Robust Exponential and Holt-Winters Smoothing algorithms are adapted. Then, to carry out accurate forecasting at a lowest level, K-Shape and K-Means clustering methods are utilized to extract similarities and identify customer consumption patterns as a second step. The third step entails developing a deep learning model based on Gated Recurrent Units to forecast the electricity consumption in each cluster. To validate the proposed method, we compared our results to the most known methods in literature like Autoregressive Integrated Moving Average, Seasonal Grey Model, LSTM networks, Temporal Convolutional Networks and two ensemble models. The results of several experiments conducted with 2000 electricity consumers during 14 years from an Algeria province (Bejaia) demonstrate that the proposed method provides remarkable prediction performances. Thus, prediction performances of the K-Shape-based clustering method reach much higher prediction accuracy. According to the MAPE metric, the results of the best predictions are equal to 2.04%. It is also notable that 87% of the clients have a considerably low prediction error.},

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

pubstate = {published},

tppubtype = {article}

}

@article{JIMENEZ-NAVARRO23c,

title = {A New Deep Learning Architecture with Inductive Bias Balance for Oil Temperature Forecasting},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00745-0},

doi = {https://doi.org/10.1186/s40537-023-00745-0},

year  = {2023},

date = {2023-05-28},

journal = {Journal of Big Data},

volume = {10},

pages = {80},

abstract = {Ensuring the optimal performance of power transformers is a laborious task in which the insulation system plays a vital role in decreasing their deterioration. The insulation system uses insulating oil to control temperature, as high temperatures can reduce the lifetime of the transformers and lead to expensive maintenance. Deep learning architectures have been demonstrated remarkable results in various fields. However, this improvement often comes at the cost of increased computing resources, which, in turn, increases the carbon footprint and hinders the optimization of architectures. In this study, we introduce a novel deep learning architecture that achieves a comparable efficacy to the best existing architectures in transformer oil temperature forecasting while improving efficiency. Effective forecasting can help prevent high temperatures and monitor the future condition of power transformers, thereby reducing unnecessary waste. To balance the inductive bias in our architecture, we propose the Smooth Residual Block, which divides the original problem into multiple subproblems to obtain different representations of the time series, collaboratively achieving the final forecasting. We applied our architecture to the Electricity Transformer datasets, which obtain transformer insulating oil temperature measures from two transformers in China. The results showed a 13% improvement in MSE and a 57% improvement in performance compared to the best current architectures, to the best of our knowledge. Moreover, we analyzed the architecture behavior to gain an intuitive understanding of the achieved solution.},

keywords = {deep learning, time series},

pubstate = {published},

tppubtype = {article}

}

@article{JIMENEZ-NAVARRO23b,

title = {PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1016/j.ins.2023.03.021},

doi = {https://www.sciencedirect.com/science/article/pii/S0020025523003183?via%3Dihub},

year  = {2023},

date = {2023-03-03},

journal = {Information Sciences},

volume = {632},

pages = {815-832},

abstract = {Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.},

keywords = {deep learning, time series},

pubstate = {published},

tppubtype = {article}

}

@article{EJIGU23,

title = {A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia},

author = {E. T. Habtemariam and K. Kekeba and M. Martínez-Ballesteros and F. Mártinez-Álvarez},

url = {https://www.mdpi.com/1996-1073/16/5/2317},

doi = {https://doi.org/10.3390/en16052317},

year  = {2023},

date = {2023-02-19},

journal = {Energies},

volume = {16},

pages = {2317},

abstract = {Renewable energies such as solar and wind power have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve current energy crises. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized Long-Short Term Memory network for forecasting wind power generation in the day ahead in the context of Ethiopia's renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performance of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics and outperformed both the baseline models and the optimized Gated Recurrent Unit architecture.},

keywords = {deep learning, time series},

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{HADJOUT22,

title = {Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market},

author = {D. Hadjout and J. F. Torres and A. Troncoso and A. Sebaa and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0360544221033090},

doi = {https://doi.org/10.1016/j.energy.2021.123060},

year  = {2022},

date = {2022-03-15},

journal = {Energy},

volume = {243},

pages = {123060},

abstract = {The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed.},

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

pubstate = {published},

tppubtype = {article}

}

@article{TORRES22b,

title = {A deep LSTM network for the Spanish electricity consumption forecasting},

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

url = {https://link.springer.com/article/10.1007/s00521-021-06773-2},

doi = {https://doi.org/10.1007/s00521-021-06773-2},

year  = {2022},

date = {2022-02-05},

journal = {Neural Computing and Applications},

volume = {34},

pages = {10533-10545},

abstract = {Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.},

keywords = {deep learning, energy},

pubstate = {published},

tppubtype = {article}

}

@article{BUI22b,

title = {Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm},

author = {K.-T. T. Bui and J. F. Torres and D. Gutiérrez-Avilés and V. H. Nhu and F. Martínez-Álvarez and D. T. Bui},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12810},

doi = {https://doi.org/10.1111/mice.12810},

year  = {2021},

date = {2021-11-24},

journal = {Computer-Aided Civil and Infrastructure Engineering},

volume = {37},

pages = {1368-1386},

abstract = {The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the deformations of the hydropower dam. The second-largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA-LSTM model has high forecasting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam's deformations.},

keywords = {deep learning, time series},

pubstate = {published},

tppubtype = {article}

}