scholarly journals Evaluating regional agricultural water resources system resilience and analyzing driving factors in the Hongxinglong Administration

2021 ◽  
Author(s):  
Dan Zhao ◽  
Dong Liu ◽  
Qiumei Wang ◽  
Qiuyuan Li ◽  
Xu Liang
Keyword(s):  
Water Resources ◽  
Driving Forces ◽  
Projection Pursuit ◽  
Principal Component ◽  
Classification Model ◽  
Key Factors ◽  
Water Resources System ◽  
Level Ii ◽  
System Resilience ◽  
Agricultural Water Resources

Abstract A Projection Pursuit Classification model optimized by the Cat Swarm Optimization algorithm (CSO-PPC) was proposed to evaluate system resilience in Hongxinglong Administration of Heilongjiang Province, China. Meanwhile, the driving forces behind resilience were analyzed using Principal Component Analysis (PCA). CSO-PPC was used to evaluate resilience for the 12 farms in Hongxinglong Administration, and PCA was applied to select the key factors driving their resilience. Results showed that the key factors were per capita water, unit area grain yield, application of fertilizer per unit cultivated area and the proportion of cultivated land, which were closely related to human production and planting area. Overall water resources system resilience improved by 2011 compared to 2005. Specifically, water resources system resilience grades for the 12 farms were divided into five levels from inferior to superior, i.e. I to V. After six years of development, the resilience of eight farms had improved. Farm Youyi and Farm 853 were upgraded from inferior level II to the best level V. However, according to the data, four farms still had low resilience that had not improved in recent years. Further results showed that the driving forces decreased from 1998 to 2003 and increased from 2003 to 2011.

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 Study on Evaluating Water Resources System Vulnerability by Reinforced Ordered Weighted Averaging Operator

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiqin Suo ◽  
Jing Zhang ◽  
Lixin He ◽  
Qian Zhou ◽  
Tengteng Kong
Keyword(s):  
Water Resources ◽  
Weighted Average ◽  
Principal Component ◽  
Weighted Averaging ◽  
Water Resource System ◽  
Water Resources System ◽  
Ordered Weighted Averaging ◽  
System Vulnerability ◽  
Ordered Weighted Averaging Operator ◽  
Handan City

Evaluating the vulnerability of a water resources system is a multicriteria decision analysis (MCDA) problem including multiple indictors and different weights. In this study, a reinforced ordered weighted averaging (ROWA) operator is proposed by incorporating extended ordered weighted average operator (EOWA) and principal component analysis (PCA) to handle the MCDA problem. In ROWA, the weights of indicators are calculated based on component score coefficient and percentage of variance, which makes ROWA avoid the subjective influence of weights provided by different experts. Concretely, the applicability of ROWA is verified by assessing the vulnerability of a water resources system in Handan, China. The obtained results can not only provide the vulnerable degrees of the studied districts but also denote the trend of water resources system vulnerability in Handan from 2009 to 2018. And the indictor that most influenced the outcome is per capita GDP. Compared with EOWA referred to various indictor weights, the represented ROWA shows good objectivity. Finally, this paper also provides the vulnerability of the water resource system in 2025 based on ROWA for water management in Handan City.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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