Ethics approval and consent to participateThe experimental scheme was formulated by the water resources allocation guidelines of Dujiangyan Management Committee and obtained personal written informed consent.

In recent years, the major geological hazard of landslides has greatly impact normal human life. Deep belief networks (DBN) is a commonly used deep learning model, and the DBN hyperparameter determination problem is the key to its application. To improve the accuracy of regional landslide susceptibility prediction, this paper introduces the particle swarm algorithm (PSO) to determine the hyperparameters of the DBN; this is applied to regional landslide susceptibility prediction. Firstly, PSO is used to optimize the hyperparameters of the DBN and obtain a set of hyperparameters with the optimal fitness function. A landslide susceptibility prediction model based on PSO-DBN is then constructed and the K-fold cross-validation method is used to determine its accuracy. The model is applied to landslide susceptibility prediction in the most impacted area of the Wenchuan earthquake to analyze the model’s accuracy. Finally, model susceptibility analysis is performed. The research results show that the final optimal model accuracy of the PSO-DBN model is 95.52%, which is approximately 28.31% and 15.35% higher than that of the logistic regression (LR) model and the common DBN model, respectively. The Kappa coefficient is 0.883, which is higher than that of the LR model. Compared with the LR model and the common DBN model, Kappa coefficient is improved by approximately 0.542 and 0.269 respectively; the area under the curve (AUC) is 0.951, which is improved by approximately 0.201 and 0.080 compared to the LR model and the common DBN model. The susceptibility of the model to the inertia factor is low, the average change in model accuracy (when the inertia factor changes by 0.1) is approximately 0.1%, and the overall stability of the model is high. The landslide susceptibility level is very high. The area includes 219 landslide points, which account for 39.2% of total landslide points. In the area with a high level of landslide susceptibility are 191 landslide points, accounting for 34.2% of total landslide points. Together, the two contain approximately 73.4% of the landslide points. This indicates that the model prediction results agree well with the spatial distribution characteristics of the landslide.

As the drought becomes more and more serious, the distribution of general water and hydropower deserves more and more attention. We establish a multi-objective programming model based on the relationship between supply and demand to manage water usage and electricity generation at the Glen Canyon and Hoover dams to address these competing interests. Finally, the distribution of regional water rights is CA (42%), CO (24%), AZ (23%), NM (7%), WY (5%) respectively. The suitable allocation of water to agriculture, industry and residences is 7.05%, 66.56%, 26.39%. In addition, the model is solved according to sufficient and insufficient conditions. In the case of sufficient conditions, the time required to meet the total daily water demand of Lake Powell is 17.848 hours, the operation period is 221.22 days, and the water delivery to Mexico is 3.38maf. The time is 14.375 hours and the delivery of water to Mexico 1.354maf.

In the present study, a spatial quantitative model of landslide hazards based on a deep belief network (DBN) is constructed. Firstly, environmental similarity-based sampling (ESBS) was used to determine the negative sampling area. Secondly, multiple data sets are constructed. Each data set contains seven landslide-conditioning factors; 70% of the data are used for training; and 30% are used for validation. The performance evaluation index of the spatial quantitative model of landslide hazards was established; that is, the AUC mean (AUCmean) was used to measure the stability of the model, and the AUC standard deviation (AUCSD) was used to measure the uncertainty of the model. Finally, the accuracy of the prediction results of the DBN model is analyzed. The results show that the area with negative sample reliability greater than 0.51 is the best negative sample sampling area, and the stability of the DBN model is maintained at a relatively good level in both the training step (AUCmean = 0.9597) and the validation step (AUCmean = 0.8897). The standard deviation of AUC is close to 0 (AUCSD = 0.0081 in the training step and AUCSD = 0.0085 in the validation step), indicating that the selected negative samples have a weak impact on the performance of the model. The susceptibility areas of very high obtained by the DBN model (landslide points in the susceptibility areas of very high accounted for 55.03%) are realistic. Therefore, the DBN model constructed in the present study is effective and can be used in the field of landslide hazard spatial prediction.

Water resource shortage is a realistic problem faced by water supply systems in river basins. Forecasting water demand is crucial for sustainable management of water supply systems. In this paper, the ARMA—DNN model is established to predict the water demand of the basin by combining the deep neural network (DNN) and the autoregressed-moving average (ARMA) mixed model. Taking the economic growth water demand and the actual social water demand as the main prediction targets, a mixed prediction model based on 14 statistical indicators is built. The model uses data from 2010 to 2020 to forecast water demand in the Minjiang River Basin. The results show that :(a) the model can accurately predict the future water consumption of the basin under the condition of actual water consumption changes; (b) The forecast of future water consumption has a significant impact on agricultural grain yield, industrial economic output value and domestic water satisfaction. In each region of the basin, agricultural grain yield and industrial economic output value and domestic water satisfaction are mutually restricted; (c) When climate conditions deteriorate and water shortages become severe, effective water demand forecasting can alleviate water demand contradictions to some extent. In a word, watershed managers need to make industrial water allocation schemes in different regions based on the forecast results of future water consumption, so as to balance the relationship among agricultural and food output, economic output value and domestic water satisfaction.