Development of Hydro-social-economic-environmental Sustainability Index (HSEESI) in Integrated Water Resources Management Abstract Sustainable development is a grand challenge of the present century, with tremendous direct and indirect implications for a range of social, economic, and environmental factors. This research proposes a water-centric framework for evaluating ‘relative’ sustainability of the status quo in a country via a new “hydro-social-economic-environmental sustainability index” (HSEESI). We test this framework across 35 countries of American Continent using national-scale surveyed data for the 2005-2017 periods. HSEESI possesses four dimensions, namely economic, social development, knowledge and technology, and health-sanitation and environment, and 12 related indicators for characterizing these dimensions. Based on the developed HSEESI scores, we assess the linkages between water resources and social-economic-environmental systems at the country level, using single and hybrid-artificial intelligence-Gene Expression Programming (GEP) methods. The former method involves all the indicators, while the latter focuses only on the most effective indicators. Further, we aggregate these analyses at three spatial scales, including American continent, North American countries, and South American countries. Our analyses show that both methods lead to approximate similar results, but the latter is preferred for larger scales as it is more cost effective. Overall, results indicate that the status of water resources in North America is relatively sustainable, whereas in South America, it is relatively unsustainable. Importantly, social development, health-sanitation, and environmental dimensions, in both North and South American continents seem to have a relatively unsustainable status, indicating that water resources systems may not have enough capacity to meet the needs of those dimensions. At the country level, our analyses show that water resources systems of Uruguay, Guyana, and Venezuela may face the highest relative unsustainability, across economic, social development, and health-sanitation and environment dimensions. The approach and the framework developed in this study can be applied in other regions around the world and with a more detailed representation of intra-country sustainability issues. It can inform managers and policymakers for sustainable planning and developing water resources projects across scales. Keywords: Artificial intelligence; Social-economic; Sustainability Index, Water management.
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Abstract Due to climate change and the decrease of surface water resources recently, groundwater resources, especially aqueducts, have special importance to meet various human requirements in arid and semi-arid regions. With the aim of Aqueduct Water Withdrawal (AWW) estimating for agricultural uses, the present research was implemented, in Golpayegan and Kashan regions of Iran; classified in non-uniform and uniform climate zones with water scarcity situation. The AWW variables were estimated based on four scenarios including: 1) aqueduct local features, 2) hydrological, 3) land-use, and 4) combined scenarios. The (Mother-well Depth (MWD), Aqueduct Channel Length (ACL)), (minimum flow rate (QMin), maximum flow rate (QMax)), and (Cultivated Area (CA), Orchard Area (OA)) variables reagent the first to third scenarios, respectively. Estimation of AWW was operated via single and Wavelet-hybrid (W-hybrid with de-noising) Soft-computing (SC) approaches, including Artificial Neural Networks (ANNs), Wavelet-ANN (WANNs), Adaptive Neuro-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 MWD, ACL, QMin, QMax, CA, and OA variables was recommended as the best model to estimate AWW variables without climate conditions' effects. With increasing levels of decomposition in wavelet approach and noise reduction, the performance of the models for estimating AWW increased. Also, the findings revealed that the implementation of the proposed method in uniform climates can have a higher performance than non-uniform climates. The achieved values of RMSE for the combined factor of WGEP models were 23.249 and 17.227 (*103 m3), for estimating AWW in Golpayegan and Kashan, respectively. The performance of WGEP was excellent (R>0.920) in the estimation of AWW in both climatic types for maximum extreme amounts. Abstracting mathematical formulation of GEP and WGEP models as part of the research finding profound effects implementing policies related to Integrated Water Resources Management (IWRM) to protect the aqueduct’s destruction by excessive consumption. Keywords: Aqueduct water withdrawal, Climatic condition, Hydrologic, Land-use, Soft-computing method
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