Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Evaluating water resource carrying capacity using the deep learning method: a case study of Yunnan, Southwest China

Water resource carrying capacity (WRCC) is an important index for measuring the relations between water resource systems and socio-economic-environmental development. In view of the difficulty in describing the complex and nonlinear relationships between the WRCC and indicators using traditional methods, this study introduces deep learning theory and proposes a novel deep neural network named WRCC-Net for WRCC assessment. Unlike typical network structures, we constructed a hierarchical structure that can indicate the index system in WRCC evaluation. Furthermore, we utilized a residual learning technique to increase the network depth for fitting the complex relationship between the WRCC state and indicators. The proposed deep learning method was applied to solve the real-world WRCC problem by taking the Yunnan province (Southwest China) as the case area. The WRCC was assessed from the following five dimensions: the water resources, social, economic, ecological environment, and coordination subsystems. Performance evaluation shows the advantages of the proposed WRCC-Net over the typical deep feed-forward network and shallow methods. Therefore, the proposed method provides a new way of evaluating the WRCC state and has potential for WRCC research. Overall, the WRCC evaluation using the WRCC-Net shows that the state of the WRCC in Yunnan constantly decreased from 2008 to 2018. These central-eastern areas in the Yunnan province, such as Kunming, Qujing, and Yuxi, are under an unfavorable capacity state. Measures, such as improving water resources management and increasing water utilization efficiency, should be considered in water resource planning in Yunnan province for the sustainable development of water resources.

Assessing the Vulnerability of Water Resources System Using VSD-SD Coupling Model: A Case of Pearl River Delta

Water plays an essential role in social and economic sustainable development, and the relationship between socio-economic development and water resources sustainable utilization has been an important issue in water resources management. We aim to propose a water resources vulnerability assessment index with the dimensions of exposure, sensitivity and adaptability, and establish a water resources system model over the Pearl River Delta network river region based on the Vulnerability Scoping Diagram (VSD) framework and System Dynamics (SD) model. The city of Zhongshan, which is located in the Pearl River Delta is used as a case study. The vulnerability of the water resources in Zhongshan from 2021 to 2050 is simulated in four different scenarios (normal, technical innovation, social economic improvement, and comprehensive development models). The results showed that the vulnerability in all four scenarios span three grades in 30 years, including moderately vulnerable, slightly vulnerable, and not vulnerable. Among them, similar trends were found between scenarios 1 and 3, and between scenarios 2 and 4. Furthermore, the vulnerability level in scenarios 2 and 4 was lower than that in scenarios 1 and 3. The vulnerability of both scenarios 2 and 4 decreased first and then increased, with the average values of 24.64 and 27.63, respectively. Scenario 2 experienced 7 years of not vulnerable (2034 to 2040) and 23 years of slightly vulnerable (2021–2033, 204–2050), scenario 4 experienced 5 years of not vulnerable (2032–2036), 21 years of slightly vulnerable (2021–2031, 2037–2046), and 4 years of moderately vulnerable (2047–2050). Although the vulnerability of scenario 4 was slightly worse than scenario 2, its adaptability to economic and social development, water resources, and water environment was much higher than scenario 2. Considering the extent of socio-economic development and the level of adaptability of the local water resources and water environment, the study concluded that the comprehensive development model is more suitable for cities in the network river area. In this scenario, sustainable water use and management can be made possible through policy regulation that encourages higher water efficiency, sewage reuse rate, and centralized sewage treatment rate.