Adaptive hybrid architecture for enhancement of the complex hydroclimatic system and assessment of freshwater security

Future freshwater security relies on hydroclimatic (HC) shifts and regimes for sustainable development. The approximation of the HC system faces major uncertainties and complexities due to the incorporation of heavy datasets, characteristics, and constraints. The proposed study focused on the parall...

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Vydané v:Journal of hydroinformatics Ročník 23; číslo 5; s. 950 - 965
Hlavní autori: Budamala, Venkatesh, Mahindrakar, Amit Baburao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London IWA Publishing 01.09.2021
Predmet:
adaptive machine learning
Adaptive sampling
Algorithms
Approximation
Climatic conditions
Community support
Emulators
Fresh water
Freshwater
Groundwater
hybrid simulators
hydroclimatology
Hydrology
Inland water environment
Learning algorithms
Machine learning
Mathematical analysis
Optimization
parallel computing
Problem solving
River basins
Rivers
Security
spatial optimization
Surface water
Sustainable development
Technology
Vulnerability
Water security
Water shortages
Water supply
Watershed management
Watersheds
Atlanta Georgia
United States > US
Chattahoochee River
ISSN:1464-7141, 1465-1734
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Abstract Future freshwater security relies on hydroclimatic (HC) shifts and regimes for sustainable development. The approximation of the HC system faces major uncertainties and complexities due to the incorporation of heavy datasets, characteristics, and constraints. The proposed study focused on the parallel computing of emulator modeling-based spatial optimization to enhance the HC systems with the perspective of future freshwater security in the Upper Chattahoochee River basin (UCR). Here, the framework compiles both physical and machine learning concepts with adaptive technology for the replication of real-world scenarios. Besides, it contains 2Emulator Model Fitting, Spatial Optimization, Parallel Computing, and Initial and Adaptive sampling to upgrade model efficiency, while UCR has inadequate groundwater and the assessment of freshwater security in UCR is more necessary for varying future climatic conditions. The results displayed that the proposed spatial optimization algorithm proved to be an effective and efficient approach in the approximation of HC models. The assessment of water security in UCR was showed in terms of scarcity and vulnerability indicators for median and low-level conditions, respectively. Moreover, this study provides the potential framework for the enhancement of physical model predictions with the incorporation of hybrid concepts for problem-solving technology which can provide significant information on HC issues.
AbstractList Future freshwater security relies on hydroclimatic (HC) shifts and regimes for sustainable development. The approximation of the HC system faces major uncertainties and complexities due to the incorporation of heavy datasets, characteristics, and constraints. The proposed study focused on the parallel computing of emulator modeling-based spatial optimization to enhance the HC systems with the perspective of future freshwater security in the Upper Chattahoochee River basin (UCR). Here, the framework compiles both physical and machine learning concepts with adaptive technology for the replication of real-world scenarios. Besides, it contains 2Emulator Model Fitting, Spatial Optimization, Parallel Computing, and Initial and Adaptive sampling to upgrade model efficiency, while UCR has inadequate groundwater and the assessment of freshwater security in UCR is more necessary for varying future climatic conditions. The results displayed that the proposed spatial optimization algorithm proved to be an effective and efficient approach in the approximation of HC models. The assessment of water security in UCR was showed in terms of scarcity and vulnerability indicators for median and low-level conditions, respectively. Moreover, this study provides the potential framework for the enhancement of physical model predictions with the incorporation of hybrid concepts for problem-solving technology which can provide significant information on HC issues.
Future freshwater security relies on hydroclimatic (HC) shifts and regimes for sustainable development. The approximation of the HC system faces major uncertainties and complexities due to the incorporation of heavy datasets, characteristics, and constraints. The proposed study focused on the parallel computing of emulator modeling-based spatial optimization to enhance the HC systems with the perspective of future freshwater security in the Upper Chattahoochee River basin (UCR). Here, the framework compiles both physical and machine learning concepts with adaptive technology for the replication of real-world scenarios. Besides, it contains 2Emulator Model Fitting, Spatial Optimization, Parallel Computing, and Initial and Adaptive sampling to upgrade model efficiency, while UCR has inadequate groundwater and the assessment of freshwater security in UCR is more necessary for varying future climatic conditions. The results displayed that the proposed spatial optimization algorithm proved to be an effective and efficient approach in the approximation of HC models. The assessment of water security in UCR was showed in terms of scarcity and vulnerability indicators for median and low-level conditions, respectively. Moreover, this study provides the potential framework for the enhancement of physical model predictions with the incorporation of hybrid concepts for problem-solving technology which can provide significant information on HC issues. HIGHLIGHTS A comprehensive framework for the integration of the physical and machine learning concepts to enhance the hydroclimatic system.; Adaptive emulator-based spatial optimization was introduced to control expensive simulations.; Parallel computing was incorporated in the framework to restrict the spatial variability in large-scale watersheds.; Assessed the future freshwater security based on the Blue/Green Water dynamics.;
Author Mahindrakar, Amit Baburao
Budamala, Venkatesh
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SubjectTerms adaptive machine learning
Adaptive sampling
Algorithms
Approximation
Climatic conditions
Community support
Emulators
Fresh water
Freshwater
Groundwater
hybrid simulators
hydroclimatology
Hydrology
Inland water environment
Learning algorithms
Machine learning
Mathematical analysis
Optimization
parallel computing
Problem solving
River basins
Rivers
Security
spatial optimization
Surface water
Sustainable development
Technology
Vulnerability
Water security
Water shortages
Water supply
Watershed management
Watersheds
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