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Adaptive Allocation Modeling for a Complex System of Regional Water and Land Resources Based on Information Entropy and its Application

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  • Kun Cheng
  • Qiang Fu
  • Xi Chen
  • Tianxiao Li
  • Qiuxiang Jiang
  • Xiaosong Ma
  • Ke Zhao

Abstract

Currently, water and land resources are treated as separate resources in allocation optimization for complex systems of water and land resources, which may have negative impacts on these water-land resource systems. In our study, an adaptive allocation model was established for a complex system of regional water and land resources using complex adaptive systems theory. The users of water and land resources were treated as adaptive agents, and the competition and synergy among various agents toward water and land resources were used as the driving forces for the evolution of the model. The model was accurately solved using a nested genetic algorithm to achieve the optimal joint allocation of regional water and land resources. A case study was conducted in the city of Kiamusze in Heilongjiang Province, and the results indicated that the evolution of the model was consistent with the actual behaviors of adaptive agents. Moreover, after the implementation of the optimized allocation results, the economic benefits in the study area were expected to increase by 3.34 %, and the comprehensive user satisfaction index regarding water increased from 0.61 to 0.73; moreover, the ecological footprint of the ecological sector increased by 5.6 %. Our results provide important guidance for achieving the sustainable use of regional water and land resources. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Kun Cheng & Qiang Fu & Xi Chen & Tianxiao Li & Qiuxiang Jiang & Xiaosong Ma & Ke Zhao, 2015. "Adaptive Allocation Modeling for a Complex System of Regional Water and Land Resources Based on Information Entropy and its Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 4977-4993, November.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:14:p:4977-4993
    DOI: 10.1007/s11269-015-1099-3
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    Cited by:

    1. Qiang Fu & Ke Zhao & Dong Liu & Qiuxiang Jiang & Tianxiao Li & Changhong Zhu, 2016. "Two-Stage Interval-Parameter Stochastic Programming Model Based on Adaptive Water Resource Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 2097-2109, April.
    2. K. Cheng & Q. Fu & J. Meng & T. X. Li & W. Pei, 2018. "Analysis of the Spatial Variation and Identification of Factors Affecting the Water Resources Carrying Capacity Based on the Cloud Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2767-2781, June.
    3. Jianwei Wang & Tianling Qin & Xizhi Lv & Yongxin Ni & Qiufen Zhang & Li Ma, 2023. "Study of Optimal and Joint Allocations of Water and land Resources for Multiple Objectives," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1241-1256, February.
    4. Jiping Yao & Guoqiang Wang & Weina Xue & Zhipeng Yao & Baolin Xue, 2019. "Assessing the Adaptability of Water Resources System in Shandong Province, China, Using a Novel Comprehensive Co-evolution Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 657-675, January.

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