Modeling Semi-Arid River-Aquifer Systems With Bayesian Networks and Artificial Neural Networks
StadisticsView Usage Statistics
MetadataShow full item record
Author/sMaldonado González, Ana Devaki; Morales Giraldo, María; Navarro Martínez, Francisco; Sánchez Martos, Francisco; Aguilera Aguilera, Pedro
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 t...
artificial neural networks