3:00 PM - 3:15 PM
[AHW22-06] Network spectrum optimization model of water resource management
Keywords:water resource management, network optimization, Fourier analysis
Climate change has intensified extreme hydrological events, significantly impacting the stability and sustainability of water resource systems. Effective water resource management is crucial for maintaining a stable water supply and enhancing drought resilience. This study aims to develop a systematic analytical framework to assess the water resource dispatching capability and evaluate how management strategies influence water supply stability under dynamic hydrological conditions.
In this research, a network flow model is utilized to analyze the water supply and demand system. Key factors such as rainfall, river flow, groundwater levels, water storage, and water demand exhibit time-dependent variations, making water allocation a dynamic optimization challenge rather than a static decision-making process. Recognizing this, the study employs Fourier spectrum analysis to examine periodic patterns and fluctuations in hydrological and water demand time series data. This research integrates network flow optimization with Fourier spectrum analysis to enable a more comprehensive understanding of the temporal characteristics of water resource availability and demand. This research provides an innovative approach to water resource management by incorporating hydrological time series analysis into decision-making frameworks. The proposed methodology enhances the ability to predict water supply fluctuations, optimize allocation strategies, and improve overall water-use efficiency in water resource systems. The findings contribute to the development of adaptive management strategies that support water resilience to climate variability, ensuring more sustainable water resource utilization in the face of increasing environmental uncertainties.
In this research, a network flow model is utilized to analyze the water supply and demand system. Key factors such as rainfall, river flow, groundwater levels, water storage, and water demand exhibit time-dependent variations, making water allocation a dynamic optimization challenge rather than a static decision-making process. Recognizing this, the study employs Fourier spectrum analysis to examine periodic patterns and fluctuations in hydrological and water demand time series data. This research integrates network flow optimization with Fourier spectrum analysis to enable a more comprehensive understanding of the temporal characteristics of water resource availability and demand. This research provides an innovative approach to water resource management by incorporating hydrological time series analysis into decision-making frameworks. The proposed methodology enhances the ability to predict water supply fluctuations, optimize allocation strategies, and improve overall water-use efficiency in water resource systems. The findings contribute to the development of adaptive management strategies that support water resilience to climate variability, ensuring more sustainable water resource utilization in the face of increasing environmental uncertainties.