| Cite this article: | 王辰璇,陈莉,张安安.基于小波-PSOSVM的陕甘宁新 农业资源可持续利用评价[J].灌溉排水学报,0,():-. |
| wangchenxuan,Chen Li,Zhang Anan.基于小波-PSOSVM的陕甘宁新 农业资源可持续利用评价[J].灌溉排水学报,0,():-. |
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| Evaluation of sustainable utilization of agricultural resources in Shaanxi-Gansu-Ningxia-Xinjiang based on wavelet-PSOSVM |
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wangchenxuan1, Chen Li2, Zhang Anan2
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1.Xiamen University of Technology;2.Anhui Jianzhu University
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| Abstract: |
| 【Objective】The quick development of the areas of Shaanxi, Gansu, Ningxia , and Xinjiang has been aided by western development. At the same time, the area's ecological resources look to be vulnerable. Agricultural resources are the foundation of agricultural development, and they must be used in a sustainable manner. 【Methods】The sustainable utilization of agricultural resources in the Shaanxi-Gansu-Ningxia-Xinjiang area was evaluated using wavelet-PSOSVM in this paper. The evaluation index system of sustainable utilization of agricultural resources is constructed from six aspects: economy, science and technology, society, natural environment, resources, and ecological governance, and combined with related data from 2018 to 2020. 【Results】(1) the mean square error of wavelet psosvm agricultural resources sustainable evaluation is MSE = 9.411 5×10-5, and the correlation coefficient is 0.968; psosvm's mean square error MSE and correlation coefficient are 0.015 3 and 0.967, respectively, under the same training and test sets. It shows that following wavelet processing, psosvm's prediction accuracy improves and convergence speeds up marginally. (2) The mean square error of wavelet SVM agricultural resources sustainable evaluation is MSE = 20.836, and the correlation coefficient is 0.748; the mean square error MSE and correlation coefficient of SVM are 30.903 and 0.634, respectively, under the same training set and test set. It also demonstrates that following wavelet processing, SVM prediction accuracy improves and convergence speeds up marginally. 【Conclusion】When we compare wavelet psosvm to wavelet SVM, psosvm to SVM, we get to the same conclusion: the prediction accuracy of PSO optimized SVM is significantly enhanced, and convergence is significantly faster. In short, following wavelet analysis of the index data, the training model's complexity is reduced, the wavelet psosvm's training time is also increased, and the prediction results are satisfactory. More scientific is the wavelet psosvm model. The evaluation results of sustainable utilization of agricultural resources in Shaanxi-Gansu-Ningxia-Xinjiang region show that Xinjiang ranks first, Gansu second, Shaanxi third and Ningxia fourth. |
| Key words: wavelet analysis; particle swarm optimization; support vector machine; resource sustainability utilization assessment |
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