China is facing a critical period of high–speed development and a transition to high–quality development. The purpose of this study is to investigate the spatial and temporal distribution of the coupling and coordination of high–quality development of urbanization and water resources in the Yellow River Basin (YRB). Firstly, we propose the concept of “High–Quality Development of Urbanization–Water Resources Coupling” and construct a high–quality development of urbanization (HQDU) system consisting of five sub–systems: innovation, coordination, greenness, openness and sharing, and a water resources system (WRS) consisting of four sub–systems: water resources background conditions, utilization capacity, management level, and pollution control. The improved coupling coordination degree model combined with the barrier degree model and clustering model is used to study the spatial and temporal distribution of the two systems in the YRB. The results show that the coupling coordination state of HQDU and WRS in the YRB improves to some extent, from “mild imbalance” to “barely coordination” in general; Henan and Ningxia are characterized by “low and fluctuating (levels) in early years and fast development in recent years”; Shanxi and Inner Mongolia develop steadily during the study period; Sichuan, Shandong, Shaanxi, and Qinghai are characterized by “low (levels) which lasted for a long time in the early stage and accelerated development in the later stage”; and Gansu Province is characterized by “high level in the early stage but insufficient momentum in later stage”. The Coordination Index of Urbanization Economic Growth Speed and the Total Wastewater Discharge are the most important obstacle factors in HQDU and WRS. This study explores the level of coordination development of HQDU and WRS as well as finds the obstacle factors in the development process of the two systems, which is an important reference value for the high–quality development of urbanization under the constraint of water resources.

In today’s world, the contradiction between water supply and demand is becoming increasingly pronounced, with a particular emphasis on the severe shortage of water for agricultural purposes. As a result, it has become imperative to promote the comprehensive reform of agricultural water pricing and increase water conservation awareness among water users. However, during the actual promotion process, the conflicting interests among stakeholders often create a behavioral game that seriously hinders the effective implementation of the agricultural water price reform. Therefore, it is crucial to address this conflict of interest and find ways to overcome it in order to ensure the successful implementation of the agricultural water pricing reform. In order to explore the dynamic evolution process of the behavior and decision making of the stakeholders in the comprehensive agricultural water price reform and the influencing factors and to propose relevant strategies to effectively promote the reform. This article constructs a three-party game model based on MA-PT theory with the government, farmers, and water supply units as the main subjects, solves the equation with the perceived benefit matrix instead of the traditional benefit matrix, and calculates the objective conditions for reaching the steady state. The simulation results show that the behavioral decision of the game subject to participate in the reform will be influenced by their perception of gains and losses, and there is a mutual influence between the three strategic choices. Therefore, the reform of agricultural water prices should consider the affordability of farmers and water supply units. The government appropriately adopts a reward system to encourage farmers and water supply units to actively participate in the reform to play a positive role and appropriate penalties for promoting the reform, the early realization of the reform goals, and the development of water-saving agriculture.

Water network construction is one of the important ways to solve complex water problems at present. It is crucial for the optimal allocation of water resources, flood control, disaster reduction, protection of water ecology, water security, and sustainable urban development. Accordingly, this study formulates an index system for assessing the efficacy of water network construction based on the Driving Force–Pressure–State–Influence–Response (DPSIR) model, taking into account the four dimensions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and intelligent water network management. The proposed index system comprises four key aspects, which are utilized to evaluate the effectiveness of water network construction efforts. Then, the game-weighting method and the matter-element extension method improved by the cloud theory established an evaluation model to evaluate and compare the water network construction effects of the two cities in Henan Province. Finally, the GM (1,1) model was used to evaluate the water network construction effects, and future trends were predicted. The results show the following: (1) On the whole, the effect of water network construction in the two cities is constantly improving; (2) There has been a significant improvement in the intelligent management of water networks. The main reason for this result is that the “Internet +” has promoted the intelligent construction of water networks; (3) The water ecological environment, flood control, and drainage capabilities continued to improve, which has largely guaranteed the basic security bottom line of urban development space; (4) The advancement and utilization of water resources has undergone gradual improvements over time, with key impact metrics centered on water supply safety factors and the development and utilization of water resources. As water supply sources continue to diversify, it is expected that the aforementioned situation will be ameliorated in the future; (5) The predicted value shows that the water network construction of the two cities can basically meet the planned value of each index when the water network construction reaches the planning level. This paper provides help to promote the sustainable use of water resources and ensure the sustainable development of cities.

Recurrent neural networks (RNNs) are widely used in reservoir operation because they can represent temporal dependencies, but their performance depends strongly on hyperparameter selection. This study compares eight evolutionary strategies—genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), differential evolution (DE), ant colony optimization (ACO), artificial electric field algorithm (AEFA), slime mould algorithm (SMA), and white shark optimization (WSO)—for optimizing the hidden-node number and initial learning rate of long short-term memory (LSTM) and gated recurrent unit (GRU) models. Under common experimental settings, ACO required the shortest computation time in all reported dataset–architecture blocks, whereas the most accurate strategy depended on the dataset, architecture, and metric. For the total dataset, ACO–LSTM and AEFA–GRU produced the lowest reported MAEs (609.876 and 582.942 m³/s, respectively); WSO–LSTM was best for flood-season MAE (948.326 m³/s), and GA–GRU was best for non-flood-season MAE (516.088 m³/s). An independent chronological holdout experiment further verifies the search boundaries and validation-fitness procedure and shows that the fitness response also varies with hydrological period and RNN architecture. The results support objective- and dataset-specific optimizer selection, subject to the limitations of a single-reservoir case study and a restricted two-variable search.