UNIVERSIDAD PABLO DE OLAVIDE
Publications
2025
Martín Solís and A. Gil-Gamboa and Alicia Troncoso
Metalearning for improving time series forecasting based on deep learning: A water case study Journal Article
In: Results in Engineering, vol. 28, pp. 107541, 2025.
Links | BibTeX | Tags: deep learning, forecasting, time series
@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}
}
A.R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
Feature Importance in Association Rule-Based Explanations for Time Series Forecasting Conference
IDEAL 26th International Conference on Intelligent Data Engineering and Automated Learning, Lecture Notes in Artificial Intelligence 2025.
Links | BibTeX | Tags: association rules, forecasting, time series, XAI
@conference{IDEAL2025_Angela,
title = {Feature Importance in Association Rule-Based Explanations for Time Series Forecasting},
author = {A.R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/chapter/10.1007/978-3-032-10489-2_20},
doi = {10.1007/978-3-032-10489-2_20},
year = {2025},
date = {2025-11-13},
urldate = {2025-11-13},
booktitle = {IDEAL 26th International Conference on Intelligent Data Engineering and Automated Learning},
series = {Lecture Notes in Artificial Intelligence},
keywords = {association rules, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
A. Gil-Gamboa and J. F. Torres and F. Martínez-Álvarez and A. Troncoso
Energy-efficient transfer learning for water consumption forecasting Journal Article
In: Sustainable Computing: Informatics and Systems, vol. 46, pp. 101130, 2025.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, time series, transfer learning
@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}
}
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.
E. T. Habtemariam and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez
A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia Journal Article
In: Neurocomputing, vol. 637, pp. 130027, 2025.
Abstract | Links | BibTeX | Tags: clustering, deep learning, energy, forecasting
@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}
}
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.
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.
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
A new metric based on association rules to assess explainability techniques for time series forecasting Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 5, pp. 4140-4155, 2025.
Abstract | Links | BibTeX | Tags: association rules, forecasting, time series, XAI
@article{TRONCOSO-GARCIA25,
title = {A new metric based on association rules to assess explainability techniques for time series forecasting},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://ieeexplore.ieee.org/document/10879535},
doi = {10.1109/TPAMI.2025.3540513},
year = {2025},
date = {2025-02-11},
urldate = {2025-02-11},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {47},
number = {5},
pages = {4140-4155},
abstract = {This paper introduces a new, model-independent, metric, called RExQUAL, for quantifying the quality of explanations provided by attribution-based explainable artificial intelligence techniques and compare them. The underlying idea is based on feature attribution, using a subset of the ranking of the attributes highlighted by a model-agnostic explainable method in a forecasting task. Then, association rules are generated using these key attributes as input data. Novel metrics, including global support and confidence, are proposed to assess the joint quality of generated rules. Finally, the quality of the explanations is calculated based on a wise and comprehensive combination of the association rules global metrics. The proposed method integrates local explanations through attribution-based approaches for evaluation and feature selection with global explanations for the entire dataset. This paper rigorously evaluates the new metric by comparing three explainability techniques: the widely used SHAP and LIME, and the novel methodology RULEx. The experimental design includes predicting time series of different natures, including univariate and multivariate, through deep learning models. The results underscore the efficacy and versatility of the proposed methodology as a quantitative framework for evaluating and comparing explainable techniques.},
keywords = {association rules, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a new, model-independent, metric, called RExQUAL, for quantifying the quality of explanations provided by attribution-based explainable artificial intelligence techniques and compare them. The underlying idea is based on feature attribution, using a subset of the ranking of the attributes highlighted by a model-agnostic explainable method in a forecasting task. Then, association rules are generated using these key attributes as input data. Novel metrics, including global support and confidence, are proposed to assess the joint quality of generated rules. Finally, the quality of the explanations is calculated based on a wise and comprehensive combination of the association rules global metrics. The proposed method integrates local explanations through attribution-based approaches for evaluation and feature selection with global explanations for the entire dataset. This paper rigorously evaluates the new metric by comparing three explainability techniques: the widely used SHAP and LIME, and the novel methodology RULEx. The experimental design includes predicting time series of different natures, including univariate and multivariate, through deep learning models. The results underscore the efficacy and versatility of the proposed methodology as a quantitative framework for evaluating and comparing explainable techniques.
2024
A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros
Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection Conference
SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications, Lecture Notes in Networks and Systems 2024.
Links | BibTeX | Tags: deep learning, forecasting, time series, XAI
@conference{SOCO24_Troncoso,
title = {Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection},
author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros},
url = {https://link.springer.com/chapter/10.1007/978-3-031-75013-7_14},
doi = {https://doi.org/10.1007/978-3-031-75013-7_14},
year = {2024},
date = {2024-10-10},
booktitle = {SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications},
pages = {139-149},
series = {Lecture Notes in Networks and Systems},
keywords = {deep learning, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso
Ground-Level Ozone Forecasting using Explainable Machine Learning Conference
CAEPIA Conference of the Spanish Association for Artificial Intelligence, vol. 14640, Lecture Notes in Artificial Intelligence 2024.
Abstract | Links | BibTeX | Tags: forecasting, time series, XAI
@conference{TRONCOSO-GARCIA24c,
title = {Ground-Level Ozone Forecasting using Explainable Machine Learning},
author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_8},
doi = {https://doi.org/10.1007/978-3-031-62799-6_8},
year = {2024},
date = {2024-06-06},
booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence},
volume = {14640},
pages = {71-80},
series = {Lecture Notes in Artificial Intelligence},
abstract = {The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.},
keywords = {forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.
R. Pérez-Chacón and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez
Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption Journal Article
In: Future Generation Computer Systems, vol. 154, pp. 397-412, 2024.
Abstract | Links | BibTeX | Tags: big data, energy, forecasting, time series
@article{PEREZ24,
title = {Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption},
author = {R. Pérez-Chacón and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X23004752},
doi = {https://doi.org/10.1016/j.future.2023.12.021},
year = {2024},
date = {2024-01-29},
journal = {Future Generation Computer Systems},
volume = {154},
pages = {397-412},
abstract = {Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.},
keywords = {big data, energy, forecasting, time series},
pubstate = {published},
tppubtype = {article}
}
Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.
C. G. García-Soto and J. F. Torres and M. A. Zamora-Izquierdo and J. Palma and A. Troncoso
Water consumption time series forecasting in urban centers using deep neural networks Journal Article
In: Applied Water Science, vol. 14, pp. 1-14, 2024.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, time series
@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}
}
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.
A. Gil-Gamboa and P. Paneque and O. Trull and A. Troncoso
Medium-term water consumption forecasting based on deep neural networks Journal Article
In: Expert Systems With Applications, vol. 247, pp. 123234, 2024.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, time series
@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}
}
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.
2023
A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez
Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting Journal Article
In: Computers and Electronics in Agriculture, vol. 215, pp. 108387, 2023.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, precision agriculture, XAI
@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}
}
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
A. Vellinger and J. F. Torres and F. Divina and W. Vanhoof
Neuroevolutionary Transfer Learning for Time Series Forecasting Conference
SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, vol. 749, Lecture Notes in Networks and Systems 2023.
Links | BibTeX | Tags: deep learning, forecasting, time series, transfer learning
@conference{VELLINGER23,
title = {Neuroevolutionary Transfer Learning for Time Series Forecasting},
author = {A. Vellinger and J. F. Torres and F. Divina and W. Vanhoof},
doi = {https://doi.org/10.1007/978-3-031-42529-5_21},
year = {2023},
date = {2023-08-31},
booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications},
volume = {749},
pages = {219-228},
series = {Lecture Notes in Networks and Systems},
keywords = {deep learning, forecasting, time series, transfer learning},
pubstate = {published},
tppubtype = {conference}
}