Identification of resilience characteristics of a regional agricultural water resources system based on index optimization and improved support vector machine

2019 ◽  
Vol 19 (7) ◽  
pp. 1899-1910 ◽  
Author(s):  
Dong Liu ◽  
Lei Xu ◽  
Qiang Fu ◽  
Mo Li ◽  
Muhammad Abrar Faiz
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Indicator System ◽  
Evaluation Index System ◽  
Practical Significance ◽  
Support Vector ◽  
Agricultural Water ◽  
Water Resources System ◽  
Agricultural Water Resources ◽  
Improved Support Vector Machine

Abstract In order to solve the gap and accuracy in the analytical methods of the resilience of a regional agricultural water resources system, a suitable evaluation index system based on the optimal index model was introduced and applied to the 15 farms in the Jiansanjiang Administration of Heilongjiang Province of China. An improved support vector machine (SVM) was used to analyze the resilience level of each farm for the selected time period. The test results showed that the indicator optimization model had the advantage of eliminating redundant indicators and ensuring the maximum content of screening indicators. The indicator system reflected all original information by 34% of initial indicators. The results showed that the particle swarm optimization-support vector machine (PSO-SVM) model had higher accuracy for the evaluation of agricultural water resource resilience through the analysis of stability and reliability of each model. The spatial pattern of resilience over selected farms was generally characterized by ‘low in the southwest and high in the northeast’. The research achievements may provide technical and theoretical support for solving problems of index optimization and analysis methods of system resilience, and have an important theoretical and practical significance for promoting the sustainable development of regional agricultural water resources systems.

scholarly journals A novel system of indicators for evaluating system resilience of regional agricultural water resources

2017 ◽  
Vol 18 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Dong Liu ◽  
Yang Ding ◽  
Qiang Fu ◽  
Dan Zhao ◽  
Muhammad Imran Khan ◽  
...  
Keyword(s):  
Water Resources ◽  
Index System ◽  
Structural Characteristics ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Agricultural Water ◽  
Water Resource System ◽  
Jiansanjiang Administration ◽  
Agricultural Water Resources ◽  
Key Indicators

Abstract Constructing a reasonable evaluation index system is important for characterizing water and land resources and for ecological restoration. To solve random and incomplete problems using a traditional evaluation index system, a novel model for evaluating regional agricultural water resources using a resilience index system was proposed. In addition, a new method for an evaluation index system and the filtering of key indicators were investigated. Based on the structural characteristics of a regional agricultural water resource system (AWRS), the model for an evaluation index system was built by constructing a hierarchical indicator architecture using a hierarchical framework model. The index weight was calculated using criteria importance through intercriteria correlation (CRITIC). Then, index completeness was ensured using principal component analysis, and the reliability of the results was tested using the analytic hierarchy process. The model was applied to the Jiansanjiang Administration of Agricultural Reclamation in Heilongjiang, China. The main results included the following: (1) the index was optimized from 46 to 32, which identified the key indicators that affect the resilience of the Jiansanjiang Administration AWRS, and (2) an evaluation index system was constructed with a completeness of 85.6%. The results of this study provide an important and practical model for studying the resilience of related resources and environmental fields.

scholarly journals Are China’s Water Resources for Agriculture Sustainable? Evidence from Hubei Province

2021 ◽  
Vol 13 (6) ◽  
pp. 3510
Author(s):  
Hao Jin ◽  
Shuai Huang
Keyword(s):  
Sustainable Development ◽  
Water Resources ◽  
Indicator System ◽  
Hubei Province ◽  
Central China ◽  
Agricultural Water ◽  
Development Capacity ◽  
Evaluation Point ◽  
Agricultural Water Resources ◽  
The Impact

We assessed the sustainability of agricultural water resources in Hubei Province, a typical agricultural province in central China, for a decade (2008–2018). Since traditional evaluation models often consider only the distance between the evaluation point and the positive or negative ideal solution, we introduce gray correlation analysis and construct a new sustainability evaluation model. Our research results show that only one city had excellent sustainable development capacity of agricultural water resources, and the evaluation value of eight cities fluctuated by around 0.5 (the median of the evaluation result), while the sustainable development capacity of agricultural water resources in other cities was relatively poor. Our findings not only reflect the differences in the natural conditions of water resources among various cities in Hubei, but also the impact of the cities’ policies to ensure efficient agricultural water use for sustainable development. The indicators and methods in this research are not difficult to obtain in most countries and regions of the world. Therefore, the indicator system we have established by this research could be used to study the sustainability of agricultural water resources in other countries, regions, or cities.

scholarly journals Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021 ◽  
Vol 13 (4) ◽  
pp. 1796
Author(s):  
Guangqi Liang ◽  
Dongxiao Niu ◽  
Yi Liang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Evolutionary Algorithm ◽  
Least Squares ◽  
Fuzzy Ahp ◽  
Indicator System ◽  
Support Vector ◽  
Type Ii ◽  
Sustainability Evaluation ◽  
Interval Type

With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

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