17:15 〜 19:15
[AHW22-P08] Optimization of Agricultural Irrigation to Enhance Climate Resilience: Integrating AI and Small Hydropower for Sustainable Water-Energy Management
キーワード：Non-dominated Sorting Genetic Algorithm III (NSGA-III), Genetic Algorithm (GA), Agricultural Irrigation System, Small HydroPower, Drought and Flood Management, Water-Food-Energy (WFE) Nexus
Abstract
In recent years, the growing impact of climate change, particularly the increasing frequency of extreme weather events such as floods and droughts, has posed significant challenges to agricultural irrigation systems. This study aims to investigate Taiwan’s Shimen Reservoir, focusing on optimizing agricultural irrigation management under extreme climate conditions. By leveraging advanced multi-objective optimization techniques, i.e., the Non-dominated Sorting Genetic Algorithm III (NSGA-III), we analyze flood and drought management strategies for the multi-objective operation of the Shimen Reservoir. The Genetic Algorithm (GA) serves as a comparative model. The optimized results are then used to evaluate improvements in agricultural water use efficiency, with the goal of enhancing the stability and efficiency of irrigation systems in response to future extreme climate events.
Furthermore, this study evaluates the impact of various scenarios (such as planned water allocation and drought index analysis) on resource use efficiency and environmental sustainability from the perspective of the Water-Food-Energy (WFE) nexus. We also explore the feasibility of integrating small hydropower generation into irrigation system for improving both irrigation efficiency and energy supply resilience. The findings highlight the transformative potential of smart irrigation systems, which can boost water use efficiency, reduce environmental strain, and provide renewable energy. By integrating small hydropower with irrigation systems, this study not only promotes energy self-sufficiency within agricultural operations but also enhances the sustainability of agricultural production by reducing reliance on external energy sources.
Under extreme climate conditions, smart irrigation systems can adaptively regulate water flow to meet crop irrigation demands, thereby reducing water waste and mitigating soil erosion risks. Ultimately, this study offers a novel, sustainable framework for agricultural irrigation systems, demonstrating the significant potential of combining irrigation with small hydropower technologies to address both environmental and energy challenges in agriculture.
In recent years, the growing impact of climate change, particularly the increasing frequency of extreme weather events such as floods and droughts, has posed significant challenges to agricultural irrigation systems. This study aims to investigate Taiwan’s Shimen Reservoir, focusing on optimizing agricultural irrigation management under extreme climate conditions. By leveraging advanced multi-objective optimization techniques, i.e., the Non-dominated Sorting Genetic Algorithm III (NSGA-III), we analyze flood and drought management strategies for the multi-objective operation of the Shimen Reservoir. The Genetic Algorithm (GA) serves as a comparative model. The optimized results are then used to evaluate improvements in agricultural water use efficiency, with the goal of enhancing the stability and efficiency of irrigation systems in response to future extreme climate events.
Furthermore, this study evaluates the impact of various scenarios (such as planned water allocation and drought index analysis) on resource use efficiency and environmental sustainability from the perspective of the Water-Food-Energy (WFE) nexus. We also explore the feasibility of integrating small hydropower generation into irrigation system for improving both irrigation efficiency and energy supply resilience. The findings highlight the transformative potential of smart irrigation systems, which can boost water use efficiency, reduce environmental strain, and provide renewable energy. By integrating small hydropower with irrigation systems, this study not only promotes energy self-sufficiency within agricultural operations but also enhances the sustainability of agricultural production by reducing reliance on external energy sources.
Under extreme climate conditions, smart irrigation systems can adaptively regulate water flow to meet crop irrigation demands, thereby reducing water waste and mitigating soil erosion risks. Ultimately, this study offers a novel, sustainable framework for agricultural irrigation systems, demonstrating the significant potential of combining irrigation with small hydropower technologies to address both environmental and energy challenges in agriculture.