Comprehensive evaluation on water resource carrying capacity in karst areas using cloud model with combination weighting method: a case study of Guiyang, southwest China

Measurement and analysis of water ecological carrying capacity in the Yangtze River Economic Belt, China

Water ecological carrying capacity (WECC) is a crucial index for measuring regional sustainable development. To investigate the evolution of WECC in the Yangtze River Economic Belt (YREB), this study constructed a comprehensive index system consisting of 23 indicators from six interconnected dimensions of water systems. The back propagation neural network (BPNN) model was used to quantify WECC in YREB from 2010 to 2021, and ArcGIS was utilized to visualize the distribution of WECC. To identify sensitive indicators under subsystems, sensitivity analysis was conducted based on the one-at-a-time (OAT) method. Additionally, time-series prediction of WECC was performed using the exponential smoothing (ES) method. Finally, the coupling coordinated degree (CCD) of subsystems in each province from 2010 to 2021 was calculated. The results indicated that the average WECC in YREB gradually increased from 2010 to 2021, with significant provincial differences. Sensitivity analysis revealed that R1, U2, Q4, S2, M3 and B1 had the most substantial impacts on the WECC of subsystems (Sub-CC). The fitting curve between the CCD and WECC showed that as CCD increased, the growth rate of WECC gradually slowed down. Based on these findings, relevant suggestions were proposed to improve WECC and promote the regional sustainable development in YREB.

The comprehensive risk assessment of the Tangjiashan landslide dam incident, China

Risk assessment for landslide dams is very important to avoid unanticipated landslide failure and calamity. Recognition of the risk of landslide dams associated with changing influencing factors is to identify the risk grade and provide early warning of oncoming failure, while quantitative risk analysis of landslide dams due to many influencing factors changing in spatiotemporal domain is currently lacking. We applied the model to analyze the risk level of the Tangjiashan landslide dam caused by the Wenchuan Ms 8.0 earthquake. The risk evaluation, obtained according to the analysis of the influencing factors located in the risk assessment grade criteria, clearly shows that the risk reaches a higher level at that moment. Our analysis shows that the risk level of landslide dams can be quantitatively analyzed with our assessment method. Our results suggest that the risk assessment system can be an effective measure to dynamically predict the risk level and provide a sufficient early warning of the oncoming hazard by analyzing the variables of influencing factors at different times.

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

A hybrid MCDM model combining Fuzzy-Delphi, AEW, BWM, and MARCOS for digital economy development comprehensive evaluation of 31 provincial level regions in China

With the deepening of a new round of technological revolution and industrial reform, digital technology has been continuously innovated and widely penetrated into various economic fields. The digital economy (DE) is gradually becoming the focus of China's economic development planning and a new engine to enhance national strength. Evaluating the development level of DE in various regions is conducive to timely discover the shortcomings in China's DE development, as well as provide an important basis for putting forward corresponding policy suggestions. This investigation established a hybrid multi-criteria decision making (MCDM) model for evaluating DE development of 31 provincial level regions in China ranging from 2015 to 2020. Firstly, the evaluation indicator system is established from digital infrastructure, integrated development, social benefits, innovation ability, and electronic-commerce dimensions containing 17 quantitative sub-criteria based on Fuzzy-Delphi method. Secondly, integrated weights of 17 sub-criteria from 2015 to 2020 are computed in terms of objective weights calculated by the anti-entropy weight (AEW) approach from 2015 to 2020 and subjective weights obtained via the best-worst method (BWM). Thirdly, MARCOS model is applied to evaluate the DE development degree of various regions in China ranging from 2015 to 2020. Case analysis illustrates that the DE development of Guangdong, Jiangsu, Zhejiang, and Beijing always maintain in the top four from 2015 to 2020, while the southwest and northwest regions in China are obviously fall behind others. And the DE development degree of various regions is primarily affected under the integrated development performance, innovation ability performance, and social benefits performance. Therefore, the backward regions should emphasize the development of software industry and information technology industry. The robustness of the proposed MCDM model combining Fuzzy-Delphi, AEW, BWM and MARCOS is discussed employing three similarity coefficients of rankings. And it is verified that the proposed MCDM model has superior robustness and validity in evaluating DE development.

Comprehensive performance evaluation of high embankments in heavy-haul railways using an improved extension model with attribute reduction algorithm

Effectively evaluating high-embankment deformation and stability is important for heavy-haul railway safety. An improved extension model with an attribute reduction algorithm was proposed for the comprehensive evaluation method. First, a hierarchical evaluation system for high embankments in heavy-haul railways was established using the attribute reduction algorithm, which includes the principal component analysis, maximum information coefficient, coefficient of variation, and improved Dempster-Shafer evidence theory. Furthermore, the improved extension model was used to evaluate high-embankment performance in heavy-haul railways. In this improved extension model, the combination weighting method, an asymmetric proximity function, and the maximum membership principle effectiveness verification were used. Finally, three high embankments in a Chinese heavy-haul railway were studied. The results illustrate that the main influencing factors for high-embankment performance in a heavy-haul railway are annual rainfall, annual temperature, and 21 other indicators. The performance of the three embankments is level III (ordinary), level II (fine), and level III (ordinary), respectively, indicating that these embankments have generally unfavourable performance. The three embankments’ performance matches field measurements, and the proposed method outperforms the Fuzzy-AHP method, cloud model, and gray relational analysis. This study demonstrates the feasibility of the proposed method in assessing the high-embankment performance under heavy axle loads.

Sustainability Assessment of Water Resources Use in 31 Provinces in China: A Combination Method of Entropy Weight and Cloud Model

As a fundamental and strategic resource, water is a crucial controlling element of ecosystem and natural environment and it plays an irreplaceable role in maintaining and promoting the sustainable development of the economy and society. To achieve the sustainable development of society, the economy and ecology, it is necessary to assess and improve the sustainability of water resources use. Based on the Human-Resource-Nature approach, this paper constructed an indicator system for the sustainability assessment of water resources use (ISSAWRU) in China from three perspectives: water resources condition, socio-economy and ecological environment. A five-level hierarchy of assessment indicators was established. Based on the entropy weight method and the cloud model which took both fuzziness and randomness into account, this paper established an entropy-cloud-based assessment model for the sustainability assessment of water resources use in 31 provinces in China in 2019. The assessment results were compared with results obtained by the TOPSIS method to test their reliability. Finally, a comprehensive and in-depth analysis of the sustainability of water resources use in China was conducted. According to the results, water resources per capita had a weighting of 0.306 and the greatest impact on the sustainable use of water resources. In addition, water structure, agricultural water use efficiency, forest coverage, and so on, had a significant impact on the sustainable use of water resources in China. The overall level of sustainability of water resources use in 31 provinces in China was not high-42% of the regions have unsustainable water resources use and there was a clear spatial distribution trend. The sustainability level of water resources use was higher in the southeast and economically developed regions. Therefore, each region should develop measures to guarantee water security based on the local conditions. This research helps policy makers to figure out the contributing factors associated with sustainability of water resources use and to set relevant rules and regulations to promote the use of water resources in a sustainable way.

Rockburst Intensity Level Prediction Method Based on FA-SSA-PNN Model

To accurately and reliably predict the occurrence of rockburst disasters, a rockburst intensity level prediction model based on FA-SSA-PNN is proposed. Crding to the internal and external factors of rockburst occurrence, six rockburst influencing factors (σθ, σt, σc, σc/σt, σθ/σc, Wet) were selected to build a rockburst intensity level prediction index system. Seventy-five sets of typical rockburst case data at home and abroad were collected, the original data were preprocessed based on factor analysis (FA), and the comprehensive rockburst prediction indexes, CPI1, CPI2, and CPI3, obtained after dimensionality reduction, were used as the input features of the SSA-PNN model. Sixty sets of rockburst case data were extracted as the training set, and the remaining 15 sets of rockburst case data were used as the test set. After the model training was completed, the model prediction results were analysed and evaluated. The research results show that the proposed rockburst intensity level prediction method based on the FA-SSA-PNN model has the advantages of high prediction accuracy and fast convergence, which can accurately and reliably predict the rockburst intensity level in a short period of time and can be used as a new method for rockburst intensity level prediction, providing better guidance for rockburst prediction problems in deep rock projects.

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.

Predictive analysis of water resource carrying capacity based on system dynamics and improved fuzzy comprehensive evaluation method in Henan Province

The water resource carrying capacity (WRCC) is a carrying capacity of natural resources. It affects the application and expansion of the carrying capacity of water resources. This subject involves various elements, such as water resources, the ecological environment system, humans and their economic and social systems, and a wider range of biological groups and their survival needs. Based on the objective recognition of the complex relationship between the water resource system, ecological environment system, and economic and social system, the support scale of water resources and the ecological environment for economic and social development is studied. Current research on the carrying capacity of water resources has mostly shifted from the previously limited support capacity of water resources to include factors such as the population, economy, and ecology, establishing the internal relationships between the economics, water resources, and ecological environment. This reflects the comprehensive carrying capacity of the entire region (or river basin) of water resources and the ecological environment system on an overall economic and social scale. Based on the conceptual connotation of the WRCC and the actual problems facing water resources in Henan Province, the paper uses a system dynamics method to develop information feedback between the four subsystems of Henan Province: economic, population, water resource, and water environment subsystems. The index system of the WRCC in Henan Province is also determined. The weight of each index is comprehensively determined by a combination weighting method of the analytic hierarchy process and an entropy weight method, and then a fuzzy comprehensive evaluation method is used to evaluate the WRCC of Henan Province under four different development models. The validation period of the model is 2010-2020, and the forecast period is 2021-2030. The results indicate that during the period 2021-2030, the WRCC of Henan Province showed a slight upward trend overall under the four models, but the increase rates were different under the different models. Among the four models, the comprehensive model's benefit was the best, which not only maintained the healthy and stable development of the economy and society but also improved the pressure on the water resources and the quality of the water environment.

Trend analysis and obstacle factor of inter provincial water resources carrying capacity in China: from the perspective of decoupling pressure and support capacity

The high distribution of water resources among provinces in China considerably impacts the development of society and economy in each region. Thus, it is of great practical significance to examine the water resources carrying capacity (WRCC) of each Chinese province. This paper constructs a comprehensive evaluation index system for the WRCC from two aspects: pressure and support. First, it analyzes dynamic changes in the WRCC of 31 Chinese provinces in China by using the decoupling model (DM). Second, it analyzes the key factors that hinder the improvement of WRCC by using the obstacle degree model (ODM). The study found that there are significant inter-provincial differences in China's WRCC. Provinces with greater natural water resources have a higher WRCC. Under the condition of similar natural water resources, WRCC in economically developed provinces is higher. From 2008 to 2015, China's overall WRCC has been increasing. Moreover, three-fifth of China's provinces can be classified as Upward-type (Upward I, Upward II, and Upward III) provinces and their WRCC is in a good state by considering the decoupling type and trend of WRCC in two periods together. The main obstacle factors hindering the improvement of the WRCC are total water resources ([Formula: see text]), water supply per capita ([Formula: see text]), total water supply ([Formula: see text]), forest cover rate ([Formula: see text]), soil erosion control area ([Formula: see text]), water consumption saving ([Formula: see text]), and water usage penetration rate ([Formula: see text]). This study can provide a scientific basis for understanding change trend of WRCC in Chinese provinces and improve their WRCC.

Optimizing Shade Cultivation Method and Irrigation Amount to Improve Photosynthetic Characteristics, Bean Yield, and Quality of Coffee in a Subtropical Monsoon Climate

Reasonable water and light management technology can improve economic benefits, coffee yield, and quality. We used cluster analysis and principal component analysis to evaluate and optimize the water and light management technology with high coffee yield, quality, and economic benefits in a subtropical monsoon climate region of China. The experiment was arranged in a randomized complete block design with two factors (3 irrigation levels × 4 shade cultivation treatments) replicated four times during 2016-2017. The irrigation levels consisted of full irrigation (FI) and two deficit irrigations (DI _L : 75% FI, DI _S : 50% FI). The shade cultivation treatments consisted of no shade cultivation (S₀) and three shade cultivation modes (S _L : intercropping with four lines of coffee and one line of banana; S _M : intercropping with three lines of coffee and one line of banana; S _S : intercropping with two lines of coffee and one line of banana). The results showed that the effects of irrigation level and shade cultivation mode on growth, crop yield, most of the photosynthetic characteristics, and nutritional quality were significant (p < 0.05). Regression analysis showed that the leaf radiation use efficiency (RUE) showed a significant negative exponential relation or logistic-curve variation with photosynthetically active radiation (PAR). The bean yield increased with an increase of the shade degree when water was seriously deficient, whereas it first increased and then decreased with an increase of the shade degree under FI and DI _L . Based on both cluster analysis and principal component analysis, the FIS _S treatment resulted in the highest comprehensive quality of coffee, followed by the FIS _M treatment; the DI _S S₀ treatment obtained the lowest quality. Compared with the FIS₀ treatment, the FIS _M treatment increased the 2-year average bean yield and net income by 15.0 and 28.5%, respectively, whereas the FIS _S treatment decreased these by 17.8 and 8.7%, respectively. To summarize, FIS _S treatment significantly improved the nutritional quality of coffee, and FIS _M treatment significantly increased the dry bean yield and economic benefits of coffee. The results of the study could provide a theoretical basis for water-saving irrigation and shade cultivation management of coffee in a subtropical monsoon climate region of China.

Assessment of coal mining land subsidence by using an innovative comprehensive weighted cloud model combined with a PSR conceptual model

Research on land subsidence is a global topic. In recent years, the environmental problems caused by coal mining have received great attention. In particular, mining land subsidence caused damage to villages, buildings, farmland, etc., which seriously threatened the mining area's living environment and ecological environment. This study proposes a pressure-state-response concept model based on mining land subsidence to build an evaluation index system in coal mines. Based on this index system, given the uncertainty in the evaluation process, the cloud model is used to represent the index weight and comprehensive evaluation calculations, which fully consider the randomness and ambiguity in the evaluation process. The mining land subsidence of several mining areas in China was evaluated and classified into three grades (slight-medium-strong). The cloud model assessment results are compared with the result of the probability integration method and the actual situation. The assessment results of the cloud model are closer to the actual situation than the probability integration method. This shows that the established mining land subsidence evaluation method based on the cloud model in this study is reasonable and feasible. The mining width and height ratio, depth and height ratio, and coal seam dip angle affect mining land subsidence. Therefore, improving the mining method to deal with the goaf reasonably and optimizing the mining design to control the influence range of mining are essential measures to reduce mining land subsidence and protect the ecological environment of mining areas.

Risk identification of coal spontaneous combustion based on COWA modified G1 combination weighting cloud model

To realize the scientific judgment of spontaneous combustion risk in the coal mine, the spontaneous combustion influence factors were analyzed from the three aspects of coal spontaneous combustion tendency, air leakage, and oxygen supply, heat storage and heat dissipation. And the basis for the evaluation of t spontaneous combustion grade was constructed. Combination ordered weighted averaging (COWA) calculation was introduced to optimizes G1 subjective weighting, and a COWA modified G1 combined weighting cloud model was proposed to identify the spontaneous combustion risk in the coal mine. Finally, the rationality of the model was verified with actual cases. The research results show that the spontaneous combustion risk level in the Lingquan coal mine is relatively safe, which is consistent with the actual situation. And the spontaneous combustion tendency of coal is the leading factor affecting spontaneous combustion risk.

Assessment on water resources carrying capacity in karst areas by using an innovative DPESBRM concept model and cloud model

The shortage of water resources in karst areas is mainly caused by the development of karst landforms, poor availability of water resources and the difficulty of utilization. To reasonably evaluate water resources carrying capacity (WRCC) of karst areas, based on characteristics of urban water resources utilization in karst areas, this study put forward DPESBRM (Driver-Pressure-Engineering water shortage-State-Ecological basis-Response-Management) concept model the first time to build an urban evaluation index system of WRCC in karst areas. Based on this index system and in allusion to uncertainties that exist during the evaluation process, a cloud model is used to represent index weights and perform comprehensive evaluation calculations, which fully considers the randomness and ambiguity of evaluation objects. WRCC from 2009 to 2018 were evaluated and were classified as five grades (Serious overload - Overload - Critical - Weak carrying capacity - Strong carrying capacity). Results proved that WRCC had improved year after year, gradually changing from a serious overload state in 2009 to a strong carrying capacity state in 2018. 2009 and 2016 were classified as I grade (serious overload). 2010 and 2011 were classified as II grade (overload). 2012, 2013 and 2015 were classified as IV grade (weak bearing capacity). 2014, 2017 and 2018 were classified as V grade (strong bearing capacity). Cloud model assessment results are compared with that of TOPSIS method, and assessment results are basically unanimous. It shows that the established WRCC evaluation method based on cloud model in this study is reasonable and feasible. Population density, urbanization rate and per capital water consumption are important driving factors affecting WRCC. Hence, strengthening the construction of water conservancy facilities, optimizing the water consumption structure, improving the efficiency of industrial water use, reducing per capital water consumption, and narrowing urban water supply and demand gap are important measures to ensure WRCC.

A Novel Hybrid Approach for Water Resources Carrying Capacity Assessment by Integrating Fuzzy Comprehensive Evaluation and Analytical Hierarchy Process Methods with the Cloud Model

The water resources carrying capacity (WRCC) shows remarkable fuzziness and randomness, which causes the uncertainty and instability of the WRCC assessment (WRCCA). In order to solve these problems, we proposed a novel hybrid approach for WRCCA, in which the fuzzy comprehensive evaluation (FCE) and analytical hierarchy process (AHP) methods were integrated with the cloud model (CM). Firstly, an evaluation indicator system of WRCC was constructed. Secondly, the AHP and FCE methods were subsequently improved with the CM. The CM was used to scale the relative importance and aggregate the judgment matrices, where the weights of the clouds were obtained. These integrations of AHP and CM greatly reduced the randomness in the weight calculation; the CM was used to describe the comment sets, calculate the membership degree matrices and determine the assignment clouds, the evaluation sets and the WRCCA index clouds were obtained. These integrations of FCE and CM effectively blurred the boundary fuzziness and gave more intuitive results. Finally, the hybrid FCE-AHP-CM approach was applied to a case study. It was concluded that the novel approach has particular advantages in dealing with the fuzziness and randomness comprehensively, and therefore could assess the WRCC and enhance the robustness and intuition of WRCCA results.