Modeling Semiarid River-Aquifer Systems with Bayesian Networks and Artificial Neural Networks

被引:3
作者
Maldonado, Ana D. [1 ]
Morales, Maria [1 ]
Navarro, Francisco [2 ]
Sanchez-Martos, Francisco [2 ]
Aguilera, Pedro A. [2 ]
机构
[1] Univ Almeria, Dept Math, Almeria 04120, Spain
[2] Univ Almeria, Dept Biol & Geol, Almeria 04120, Spain
关键词
Bayesian networks; artificial neural networks; groundwater temperature; classification; semiarid areas; GROUNDWATER RECHARGE; DECISION-SUPPORT; NATURAL TRACER; TEMPERATURE; HEAT; PRECIPITATION; PREDICTION; TRANSIENT; FLUXES; FLUCTUATIONS;
D O I
10.3390/math10010107
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页数:17
相关论文
共 50 条
[41]   Artificial Neural Networks for Navigation Systems: A Review of Recent Research [J].
Jwo, Dah-Jing ;
Biswal, Amita ;
Mir, Ilayat Ali .
APPLIED SCIENCES-BASEL, 2023, 13 (07)
[42]   Application of artificial neural networks in reproductive medicine [J].
Yuan, Guanghui ;
Lv, Bohan ;
Hao, Cuifang .
HUMAN FERTILITY, 2023, 26 (05) :1195-1201
[43]   USING ARTIFICIAL NEURAL NETWORKS IN E-LEARNING SYSTEMS [J].
Susnea, Elena .
UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2010, 72 (04) :79-88
[44]   Artificial neural networks in lightning location systems [J].
Bermudez, JL ;
Piras, A ;
Rubinstein, M .
1ST INTERNATIONAL SYMPOSIUM ON NEURO-FUZZY SYSTEMS - AT'96, CONFERENCE REPORT, 1996, :177-178
[45]   Artificial neural networks in neurosurgery [J].
Azimi, Parisa ;
Mohammadi, Hasan Reza ;
Benzel, Edward C. ;
Shahzadi, Sohrab ;
Azhari, Shirzad ;
Montazeri, Ali .
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, 2015, 86 (03) :251-256
[46]   Monitoring river water quality through predictive modeling using artificial neural networks backpropagation [J].
Novianta, Muhammad Andang ;
Syafrudin ;
Warsito, Budi ;
Rachmawati, Siti .
AIMS ENVIRONMENTAL SCIENCE, 2024, 11 (04) :649-664
[47]   Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques [J].
Daud, Suleman ;
Shahzada, Khan ;
Tufail, M. ;
Fahad, M. .
ADVANCES IN CIVIL ENGINEERING, PTS 1-6, 2011, 255-260 :679-+
[48]   Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava [J].
Marijana Hadzima-Nyarko ;
Anamarija Rabi ;
Marija Šperac .
Water Resources Management, 2014, 28 :1379-1394
[49]   Modeling the Biocatalytic Method of Lipid Extraction Using Artificial Neural Networks [J].
Shafrai, Anton V. ;
Prosekov, Alexander Yu. ;
Vechtomova, Elena A. .
INFORMATION, 2023, 14 (08)
[50]   Spatial Modeling of Natural Phenomena and Events with Artificial Neural Networks and GIS [J].
Noack, Silke ;
Barth, Andreas ;
Irkhin, Alexey ;
Bennewitz, Evelyn ;
Schmidt, Frank .
INTERNATIONAL JOURNAL OF APPLIED GEOSPATIAL RESEARCH, 2012, 3 (01) :1-20