Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks
In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling t...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/13264 |
| _version_ | 1789406441540419584 |
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| author | Maldonado González, Ana Devaki Morales Giraldo, María Navarro Martínez, Francisco Sánchez Martos, Francisco Aguilera Aguilera, Pedro |
| author_facet | Maldonado González, Ana Devaki Morales Giraldo, María Navarro Martínez, Francisco Sánchez Martos, Francisco Aguilera Aguilera, Pedro |
| author_sort | Maldonado González, Ana Devaki |
| collection | DSpace |
| description | In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-13264 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-132642023-04-12T18:58:09Z Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks Maldonado González, Ana Devaki Morales Giraldo, María Navarro Martínez, Francisco Sánchez Martos, Francisco Aguilera Aguilera, Pedro Bayesian networks artificial neural networks groundwater temperature classification semiarid areas In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis. 2022-02-11T11:22:26Z 2022-02-11T11:22:26Z 2021-12-29 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/13264 10.3390/math10010107 en https://www.mdpi.com/2227-7390/10/1/107 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | Bayesian networks artificial neural networks groundwater temperature classification semiarid areas Maldonado González, Ana Devaki Morales Giraldo, María Navarro Martínez, Francisco Sánchez Martos, Francisco Aguilera Aguilera, Pedro Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks |
| title | Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks |
| title_full | Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks |
| title_fullStr | Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks |
| title_full_unstemmed | Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks |
| title_short | Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks |
| title_sort | modeling semi-arid river-aquifer systems with bayesian networks and artificial neural networks |
| topic | Bayesian networks artificial neural networks groundwater temperature classification semiarid areas |
| url | http://hdl.handle.net/10835/13264 |
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