Prediction Spatial Distribution of Soil Organic Matter Based on Improved BP Neural Network with Optimized Sparrow Search Algorithm

被引：3
|
作者：
Hu Z.-R. [1 ]
Zhao W.-F. [1 ]
Song Y.-X. [2 ]
Wang F. [3 ]
Lin Y.-M. [3 ]
机构：
[1] Ningxia Survey and Monitoring Institute of Land Resources, Yinchuan
[2] Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming
[3] School of Geography and Planning, Ningxia University, Yinchuan
来源：
Huanjing Kexue/Environmental Science | 2024年 / 45卷 / 05期
关键词：
BP neural network; digital soil mapping; optimize; soil organic matter （SOM）; sparrow search algorithm; Weining Plain;
D O I：
10.13227/j.hjkx.202305203
中图分类号：
学科分类号：
摘要：
Soil organic matter is an important indicator of soil fertility，and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study，we analyzed the organic matter content of 1 690 soil surface layers（0-20 cm）and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics，deterministic interpolation，geostatistical interpolation and machine learning，respectively，and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models. The results showed that the average SOM content of the surface soil of the Weining Plain was 14. 34 g·kg-1，and the average soil organic matter variation across 1 690 sampling points was 34. 81%，indicating a medium degree of variability. The results also revealed a spatial distribution trend，with low soil organic matter content in the northeast and southwest，high soil organic matter on the left and right banks of the Yellow River in the middle，and relatively high soil organic matter in the sloping terrain of the Weining Plain. The four types of methods in order of high to low prediction accuracy were the machine learning method，geostatistical interpolation method，deterministic interpolation method，and classical statistical method. Through comparison，the BP neural network that was improved based on the optimized sparrow search algorithm had the best prediction accuracy，and the optimized sparrow search algorithm had better convergence accuracy，avoided falling into local optimization，prevented data overfitting，and had better prediction ability. This optimization algorithm can improve the accuracy of SOM prediction and has good application prospects in soil attribute prediction. © 2024 Science Press. All rights reserved.
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页码：2859 / 2870
页数：11
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