A stochastic simulation-based method for predicting the carrying capacity of agricultural water resources

Li He, Yu Du, Menglong Yu, Hao Wen, Haochen Ma and Ying Xu

Agricultural Water Management, 2024, vol. 291, issue C

Abstract: The prediction of the agricultural water resources carrying capacity (AWRCC) is important for evaluating the ecological security of water resources and ensure healthy and sustainable socio-economic development. However, the AWRCC is influenced by many important factors such as water resources volume, socio-economic structure and technology level. The existing prediction methods were mainly based on the policies and economic activities under predefined scenarios. It is difficult to fully reflect the future AWRCC and generate regulation schemes based on AWRCC target requirements. The paper proposed a stochastic simulation method to simulate the evaluation indicators, which were selected based on principal component analysis (PCA) and iterative strategy, and structured a support vector machine model to solve the problem of complicated calculations to realize the probability distribution prediction of the AWRCC. Furthermore, the important indicators affecting the improvement of the AWRCC were explored, and finally a regulation schemes that met the AWRCC target were generated. The results of the calculations for Zhangjiakou, Hebei Province, indicated that there is a 97.5% probability that the AWRCC evaluation value will be Level III in 2025 and there is a 2.5% probability that it will be Level II. The probabilities of achieving close to saturated, transition, and close to weakly carriable states for the corresponding Level III are 27.5%, 61%, and 9%, respectively, and the root mean square error of the prediction is 0.0057. In addition, by adjusting the important indicators, the probability of the AWRCC reaching Level IV is 72.5%, and AWRCC improves from Level III to Level IV. The proposed method can achieve higher efficient and accurate AWRCC evaluation, which can better meet the requirements of modern water resources management, effectively reduce the misjudgment risk and provide support for water resources regulation.

Keywords: Agricultural water resources carrying capacity; Stochastic simulation; Monte Carlo; Support vector machine; Regulation scheme (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037837742300495X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:291:y:2024:i:c:s037837742300495x

DOI: 10.1016/j.agwat.2023.108630

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().