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Condition Information Entropy and Rough Set Method Based on Particle Swarm Optimization Applied in the Natural Quality Evaluation of Cultivated Land

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  • Hongmei Yu

    (Science and Technology on Complex Land System Simulation Laboratory, Beijing 100012, China)

  • Zhaokun Yu

    (School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Xubing Zhang

    (School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

Abstract

The evaluation of the natural quality of cultivated land is crucial for preserving arable land and achieving a balance between the quantity and quality of arable land. Therefore, a timely assessment of the natural quality of cultivated land is needed to monitor its changes. However, current methods often focus on a single specified crop, neglecting the variations that occur across different specified crops. Since the indicator weight recognition method is only suitable for a single crop, this paper proposes a novel model evaluating the natural quality of cultivated land based on the method of “hidden light–temperature index and yield ratio coefficient”. In addition, the condition information entropy and rough set method based on particle swarm optimization (CIERS-PSO) were proposed to evaluate the natural quality of cultivated land in Enshi. Firstly, condition information entropy and rough set are adopted to determine the importance of the indicator automatically. Then, particle swarm optimization (PSO) is utilized to obtain the optimal weights of the first-level and second-level indicators. Finally, the proposed model and evaluation method were adopted to evaluate the natural qualities of the cultivated land. The experimental results demonstrated that the combination of the “hidden light–temperature index and yield ratio coefficient” model and the CIERS-PSO method can automatically identify the indicator weights for the evaluation of natural quality in multi-crop cultivated land. It could obtain better evaluation accuracy even if the sample size is small.

Suggested Citation

  • Hongmei Yu & Zhaokun Yu & Xubing Zhang, 2024. "Condition Information Entropy and Rough Set Method Based on Particle Swarm Optimization Applied in the Natural Quality Evaluation of Cultivated Land," Sustainability, MDPI, vol. 16(8), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3484-:d:1380211
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    1. Mohamed S. Shokr & Mostafa. A. Abdellatif & Ahmed A. El Baroudy & Abdelrazek Elnashar & Esmat F. Ali & Abdelaziz A. Belal & Wael. Attia & Mukhtar Ahmed & Ali A. Aldosari & Zoltan Szantoi & Mohamed E. , 2021. "Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    2. Di Zhao & Junyu Dong & Shuping Ji & Miansong Huang & Quan Quan & Jian Liu, 2020. "Effects of Contemporary Land Use Types and Conversions from Wetland to Paddy Field or Dry Land on Soil Organic Carbon Fractions," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    3. Mengmeng Tang & Cuiting Wang & Chunyang Ying & Shuai Mei & Tong Tong & Youhua Ma & Qiang Wang, 2023. "Research on Cultivated Land Quality Restriction Factors Based on Cultivated Land Quality Level Evaluation," Sustainability, MDPI, vol. 15(9), pages 1-12, May.
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