Prediction of groundwater level fluctuations under climate change based on machine learning algorithms in the Mashhad aquifer, Iran. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Panahi, Ghasem; Hassanzadeh Eskafi, Mahya; Faridhosseini, Alireza; Khodashenas, Saeed Reza; Rohani, Abbas
• Is Part Of: Journal of water and climate change. Volume 14:Issue 3(2023)
• Page Start: 1039
• Page End: 1059
• Publication Date: 2023-03-04
• Subjects: climate change -- CMIP6 model -- groundwater level -- machine learning algorithms; Water -- Periodicals; Hydrology -- Periodicals; Climatic changes -- Periodicals; Climatic changes; Hydrology; Water; Electronic journals; Periodicals; 333.9116
• DOI: 10.2166/wcc.2023.027
• Languages: English
• ISSNs: 2040-2244

Abstract

Abstract: The purpose of this study is the projection of climate change's impact on the Groundwater Level (GWL) fluctuations in the Mashhad aquifer during the future period (2022–2064). In the first step, the climatic variables using ACCESS-CM2 model under the Shared Socio-economic Pathways (SSPs) 5–8.5 scenario were extracted. In the second step, different machine learning algorithms, including Multilayer Perceptron Neural Network (MLP), Adaptive Neuro-fuzzy Inference System Neutral Network (ANFIS), Radial Basis Function Neural Network (RBF), and Support Vector Machine (SVM) were employed for the GWL fluctuations time series prediction under climate change in the future. Our results point out that temperatures and evaporation will increase in the autumn season, and precipitation will decrease by 26%. The amount of evaporation will increase in the winter due to an increase in temperature and a decrease in precipitation. The results showed that the RBFNN model had an excellent performance in predicting GWL compared to other models due to the highest value of R² (R² = 0.99) and the lowest value of RMSE, which were 0.05 and 0.06 meters in training and testing steps, respectively. Based on the result of the RBFNN model, the GWL will decrease by 6.60 meters under the SSP5-8.5 scenario. HIGHLIGHTS: The CMhyd model was used to extract climatic variables from the ACCESS-CM2 model. Temperatures and evaporation will increase and rainfall will decrease. The Radial Basis Function Neural Network model had an excellent performance in GWL prediction. The GWL will decrease by 6.60 m under the SSP5–8.5 scenario in the Mashhad aquifer. Graphical Abstract