Water Resource Carrying Capacity Based on Water Demand Prediction in Chang-Ji Economic Circle

Optimization of land use planning under multi-objective demand-the case of Changchun City, China

Modeling and scenario analysis are the core elements of land use change research, and in the face of the increasingly serious ecological and environmental problems in urbanization, it is important to carry out land use simulation studies under different protection constraints for scientific planning and policy formulation. Taking Changchun City, the capital of Jilin Province, a pilot national eco-province, as an example, a CLUE-S model with coupled landscape ecological security patterns was constructed to predict and simulate the land use structure and layout under multi-objective optimization scenarios in the planning target year (2030), and the results were analyzed based on landscape index evaluation. The study found the following: (i) the proportion of ecological land area under low, medium, and high security levels in the study area was 8.7%, 64.8%, and 26.5%, respectively; (ii) under the current development trend scenario, the trend of increasing fragmentation of cultivated land patches in Changchun in 2030 will remain unchanged, with construction land spreading along the periphery in a compact and continuous pattern, while ecological land will be seriously encroached upon; and (iii) in the 2030 multi-objective optimization scenario, land use patches of all types will begin to show a tendency to cluster, with less landscape fragmentation and more connectivity, while cultivated land and construction land will also begin to converge and do not deteriorate as a result of spatial conflicts over ecological land.

Comprehensive evaluation of water ecological environment in watersheds: a case study of the Yangtze River Economic Belt, China

The Yangtze River Economic Belt, an inland economic zone with global influence, has shown a trend of prosperous economic development in recent years. Economic development, water pollution, resource depletion, and other environmental problems continue to emerge. The steady state of the water ecological environment is an important aspect of ecological security. To investigate the regional water ecological security state, this study constructs a comprehensive evaluation indicator system within the framework of "driving force-carrying source-state-management" (DCSM). The entropy weight method was used to determine the weight of each indicator, and the weighted rank sum ratio model was introduced to classify the water ecological environment of the Yangtze River Economic Belt from 2010 to 2019. Finally, an adversarial interpretative structure model is used to refine the ranking of each region. The results show that the bearing state and driving force subsystems are closely related to the water ecological environment. The top three indicators are wastewater discharge of industrial added value of 10,000 yuan, water consumption per 10,000 yuan of industrial gross product, and water consumption per 10,000 yuan of tertiary gross domestic product. In addition, there are clear differences in the water ecological environment of the Yangtze River Economic Belt. The classification results show that Zhejiang and Jiangsu are rated as "excellent''; Yunnan, Guizhou, Anhui, and Jiangxi are in the "good" level; and Sichuan, Hunan, Chongqing, and Hubei are in the "medium" level. Shanghai is "poor." As a whole, the downstream is superior, the upstream is second, and the midstream is poor in an asymmetric "U"-shaped distribution. During the study period, the overall state of water ecology in the Yangtze River Economic Belt was at a medium level and has not yet reached a safe and steady state. The performance of areas with traditional industrialization as the main development path was poor. Therefore, it is necessary to pay attention to the overall water ecological security in the basin in the future, strengthen the regulatory role of the government's water ecological management, promote reform of traditional industries and resource-based regions, and achieve the sustainable development of the water ecological environment.

A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

Most of the water quality indicators that affect the results of river water quality assessment are gray and localized, thus the correlation between water quality indicators can be calculated using gray correlation analysis (GRA).However, GRA takes equal weighting for water quality indicators and does not take into account the weighting of the indicators. Therefore, this paper proposes a river water quality assessment method based on improved grey correlation analysis (ACGRA) andparticle swarm optimization multi-classification support vector machine (PSO-MSVM) for assessing river water environment quality. Firstly, the combination weights of water quality indicators were calculated using Analytic Hierarchy Process (AHP)AHP and Criteria Importance Though Intercrieria Correlation (CRITIC)CRITIC, and then the correlation between water quality indicators was calculated for feature selection. Secondly, the PSO-MSVM model was established using the water quality indicators obtained by ACGRA as input parameters for water environment quality assessment. The river water environment assessment methods of ACGRA and PSO-MSVM were applied to the evaluation of water environment quality in different watersheds in the country. Accuracy, precision, recall and root mean square errorRMSE were also introduced as model evaluation criteria. The results show that the river water environment assessment methods based on ACGRA and PSO-MSVM can evaluate the water environment quality more accurately.

Using a Grey Niche Model to Predict the Water Consumption in 31 Regions of China

Regional development brings significant changes in industrial structure and water consumption. Researching the trend in water consumption by changes in industrial structure can promote water conservation. The grey niche model describes the industrial changes in China and analyzes the water consumption of different leading industries. Using data from 2014 to 2019, and taking the economy as the influencing reason and the industrial niche as the weight, water consumption was predicted. The average percentage errors of the prediction results were all less than 0.1%. While improving the forecasting accuracy, the water consumption forecasting has been strengthened. The calculation results show that regional industry is undergoing transformation, and tertiary industry is rising in the national economy. The successful implementation of industrial water-saving measures has kept the water consumption of industrially developed cities stable but the rapid development of tertiary industries will increase water consumption. Incorporating changes in industrial structure into water use analysis allows the Chinese government to draft water conservation policies for various industries.