Multivariate modeling of agricultural river water abstraction via novel integrated-wavelet methods in various climatic conditions

Alireza Emadi (), Reza Sobhani (), Hossein Ahmadi (), Arezoo Boroomandnia (), Sarvin Zamanzad-Ghavidel () and Hazi Mohammad Azamathulla ()
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Alireza Emadi: Sari Agricultural Science and Natural Resources University
Reza Sobhani: Sari Agricultural Science and Natural Resources University
Hossein Ahmadi: University of Tehran
Arezoo Boroomandnia: University of Tehran
Sarvin Zamanzad-Ghavidel: University of Tehran
Hazi Mohammad Azamathulla: University of the West Indies

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 4, No 16, 4845-4871

Abstract: Abstract Water resource management in the agricultural sector is a necessity that profoundly impacts water resources conservation for future generations. With the aim of rivers water abstraction (RWA) estimating for agricultural uses, the current research was implemented, in Hashtgerd and Zarand-Saveh sub-catchments of Iran; classified in Csa and Bsk-Csa climate categories with water scarcity situation. The RWA variables were estimated based on morphological, hydrological, and land-use characteristics and their combinations. Estimation of RWA was operated by applying single and integrated-wavelet (integrated-W with noise reduction) soft-computing methods, including Artificial Neural Networks (ANNs), Wavelet-ANN (WANNs), Adaptive Neural Fuzzy Inference System (ANFIS), Wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet-GEP (WGEP). The WGEP model's efficiency with the hybrid characteristics of river width (RW), river depth (RD), minimum flow rate (QMin), maximum flow rate (QMax), average flow rate (QMean), cultivated area (CA), and orchard area (OA) variables, was recommended as the best model to estimate RWA variables without climate conditions' effects. The obtained values of RMSE for hybrid characteristic of WGEP models were 26.310 and 61.256 (*103 m3), for estimating RWA in Hashtgerd and Zarand-Saveh, respectively. The efficiencies of WGEP were excellent (R > 0.900) in the estimation of RWA in both climatic classes for maximum extreme values. Extracting mathematical formulation as part of the study’s result profoundly impacts implementing policies related to integrated water resources management (IWRM). Also, the modeling of the variables affected the optimal rate of agricultural RWA for advance measures of balancing policies improvement in supply and demand of water resources in the agricultural sector, modification of cultivation patterns to save water consumption, providing food security to communities, and reforming the pattern of riverbeds that are recommended for reaching the goals of IWRM.

Keywords: Soft-computing methods; Estimating; Water abstraction; Integrated-wavelet (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10668-021-01637-0

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