Groundwater spring potential prediction using a deep-learning algorithm

• Autorzy: Moughani Solmaz Khazaei , Osmani Abdolbaset , Nohani Ebrahim , Khoshtinat Saeed , Jalilian Tahere , Askari Zahra , Heddam Salim , Tiefenbacher John P. , Hatamiafkoueieh Javad

Identyfikatory

DOI

10.1007/s11600-023-01053-0

• Języki publikacji: EN

Abstrakty

EN

Information about water resources is crucial for sustainable development, and this issue is considered to be one of the most important concerns worldwide due to rapid industrialization and population growth. Countries in the semiarid region of the western Asia, like Iran, are dependent on groundwater resources so access to these resources is vital. This study maps surface spring potential on the Nourabad-Koohdasht Plain of Iran using a deep-learning algorithm called convolutional neural network (CNN), and the result was compared to predictions made with five advanced data-mining models: logistic model tree (LMT), LMT hybridized with bagging (BA-LMT), LMT hybridized with dagging (DA-LMT), LMT hybridized with random subspace (RS-LMT), and LMT hybridized with AdaBoost (AB-LMT). Frequency ratio was used to assess the strengths of relationships of each subclass layer to groundwater presence and evidential belief function revealed their effects on model uncertainty. The locations of 2463 springs were determined and showed that the northern part of the plain has the highest groundwater potential based on the density of springs. The data representing each of the spring locations were used for prediction modeling. Receiver operating characteristic (ROC) and area under the ROC curve (AUC) were used to evaluate the strengths of the predictions produced by the models. The results show that CNN (AUC = 0.885) provided the best prediction of spring locations. AB-LMT (AUC = 0.877) was second best, and BA-LMT (AUC = 0.876), DA-LMT (AUC = 0.856), RS-LMT (AUC = 0.846), and the standalone LMT model (AUC = 0.827) followed in rank. It can be concluded that the hybrid LMT models increased the predictive strength of the standalone LMT model when used to predict spring locations. These hybrid modeling methods may be used to improve sustainable groundwater management in the study region and in other regions as well.

Słowa kluczowe

EN

groundwater potential mapping; deep learning; tree-based models; machine learning; Iran

PL

mapowanie potencjału wód podziemnych; głębokie uczenie; modele oparte na drzewach; uczenie maszynowe; Iran

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2024
• Tom: Vol. 72, no. 2
• Strony: 1033--1054
• Opis fizyczny: Bibliogr. 97 poz.

Twórcy

autor

Moughani Solmaz Khazaei

• Department of Civil Engineering, Faculty of Hydraulic Structures, The Institute of Higher Education of Bonyan, Shahinshahr, Isfahan, Iran

autor

Osmani Abdolbaset

• Agricultural Jihad Organization of Kurdistan Province, Zarrineh branch, Zarrineh, Iran

autor

Nohani Ebrahim

• Material and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran

autor

Khoshtinat Saeed

• Department of Water Science, Urmia Municipality, Urmia, Iran

autor

Jalilian Tahere

• Department of Geography, Isfahan University, Isfahan, Iran

autor

Askari Zahra

• Department of Water Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

autor

Heddam Salim

• Faculty of Science, Agronomy Department, Hydraulic Division, University 20 Aout 1955 SKIKDA, Skikda, Algeria

autor

Tiefenbacher John P.

• Department of Geography and Environmental Studies, Texas State University, San Marcos, USA

autor

Hatamiafkoueieh Javad

• Department of Mechanics and Control Processes, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow, Russian Federation 117198

• https://orcid.org/0000-0003-1237-4467

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