Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

• Published in: Journal of hydrology (Amsterdam) Vol. 636; p. 131220
• Main Author: Nasiri Khiavi, Ali
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024
• Subjects: Artificial Intelligence (AI); base flow; case studies; decision support systems; Environmental Flow; hydrologic data; Iran; Optimal Decision Making; prioritization; Python Programming Language; regression analysis; rivers; subwatersheds; support vector machines; temperature; water management; Water Resources Management (WRM); Iran; Environmental Flow; Artificial Intelligence (AI); Python Programming Language; Water Resources Management (WRM); Optimal Decision Making
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2024.131220

•Base Flow modeling with conjunct application of MCDM and machine learning.•Combining MCDM and machine learning algorithms improved Base Flow modeling.•Game theory algorithm performed better than BWM in Base Flow modeling.•Base Flow generation potential was higher in the watershed upstream. The study of base flow is essential for sustainable water management. By understanding the dynamics of base flow, policymakers can make informed decisions to ensure the long-term health and availability of water resources. This study was conducted with the aim of modeling the spatial changes of Base Flow Generation Potential (BFGP) using integrated Machine Learning Algorithms (MLAs) including Decision Tree Regression (DTR), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF), Simple Linear Regression (SLR), Support Vector Machine (SVM) and Support Vector Regression (SVR) and optimal Multi-Criteria Decision Making (MCDM) methods including Best-Worst Method (BWM) and Fallback bargaining in the Cheshmeh-Kileh watershed, Iran. BFGP conditioning factors were selected for the Sub-Watershed (SW) and weighed using MCDM (BWM and Fallback bargaining algorithm) methods. BFGP maps of five classes were generated and analyzed. MCDM methods were also integrated with ML algorithms for optimum performance evaluation of the integrated unit. Based on the results, in the integrated model of BWM and Fallback bargaining algorithm with ML algorithms, SVR and RF algorithms was selected as the optimal model, respectively. In BWM in the Sehezar River, the upstream had the highest BFGP. The pattern of spatial changes based on the Fallback bargaining algorithm in the upstream of Sehezar River was similar to BWM. The combined approach of RF-GTA with the factors of minimum temperature (Tn), NDSI and elevation showed the highest correlation. It was observed that that the Fallback bargaining algorithm and BWM of the MCDM exhibited similarity in output of regarding the spatial change patterns of the BFGP. Also, Fallback bargaining exhibited a 67% similarity with the integrated RF-GTA in BFGP prioritization of the SWs, while BWM performed poorly. This suggests that the Fallback bargaining algorithm was more effective than BWM of the MCDM methods for SWs BFGP prioritization. The general summary indicated that the combined use of MCDM and MLAs were valuable tools in BF studies and helped in better understanding the complex hydrological processes. The findings of our research may be of help to Iran Water Resources Research Company using hydrological data arising from base flow studies to support their analysis. In conclusion, understanding the BF characteristics of SWs allows watershed managers to effectively prioritize conservation and restoration efforts aimed at protecting and enhancing the natural flow regime.