Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran

• Published in: Neural computing & applications Vol. 36; no. 20; pp. 12609 - 12624
• Main Authors: Mirzania, Ehsan, Achite, Mohammed, Elshaboury, Nehal, Katipoğlu, Okan Mert, Saroughi, Mohsen
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Aquifers; Aridity; Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Effectiveness; Evaporation; Groundwater levels; Image Processing and Computer Vision; Indicators; Irrigation; Lead time; Original Article; Probability and Statistics in Computer Science; Root-mean-square errors; Semi arid areas; Water resources management; Iran; Tabriz plain; Artificial neural networks (ANN); Egret swarm optimization algorithm (ESOA); Wild horse optimizer (WHO); Groundwater level
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09681-3

Predicting the groundwater level (GWL) is essential in water resource management and irrigation planning in arid and semi-arid areas. In this study, an artificial neural network (ANN) was combined with newly developed wild horse optimizer (WHO) and egret swarm optimization algorithm (ESOA) techniques to predict a one month lead-time GWL in the Tabriz plain of Iran. For the prediction of the GWL, the number of months and years, the one month lag of average temperature, evaporation, precipitation, and GWL were used as inputs. Model performances were compared using root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), coefficient of determination ( R 2 ), and relative strength ratio (RSR) statistical indicators and scatter diagrams, time series graph, violin graph, and Taylor diagram. As a result of the analysis, the most successful estimation results were obtained with the input combinations of year, month, average temperature, evaporation, precipitation, and GWL ( t  − 1) for the prediction of the one month lead-time GWL. According to the results of evaluation indicators in the testing phase, ANN with ( R 2  = 0.871, RMSE = 0.306 (m), NSE = 0.832, and RSR = 0.410), WHO–ANN ( R 2  = 0.932, RMSE = 0.200 (m), NSE = 0.929, and RSR = 0.267), and ESOA–ANN (R 2  = 0.952, RMSE = 0.164 (m), NSE = 0.951, and RSR = 0.220). In addition, it was revealed that the ESOA–ANN hybrid model showed higher prediction success than the WHO–ANN and standalone ANN models. The study outputs contribute to decision-makers and planners for controlling land subsidence, assessing GWL and aquifer compaction, irrigation planning, and effective management of water resources.