Method and system for forecasting spatial distribution of heavy metals in soil

Abstract

The embodiment of the invention provides a method and a system for forecasting the spatial distribution of heavy metals in soil, and relates to the technical field of environmental monitoring. The method comprises the following steps: measuring a content value of heavy metals in soil of a soil sample at a sampling point; constructing a training data set; constructing a GBRT (Gradient Boosting Regression Tree) model by adopting an auxiliary feature and the content of heavy metals in the soil as variables, and training the GBRT model by using the training data set to obtain a trained GBRT model; constructing a data set to be measured; inputting the data set to be measured into the trained GBRT model and outputting a content value of heavy metals in the soil corresponding to a point to be measured. As a GBRT algorithm iteratively integrates characteristics of a number of weak regression devices, and auxiliary features are rationally applied, the method has a good generalization ability and is conducive to popularization and application.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of environmental monitoring, and more specifically, to a method and system for predicting the spatial distribution of soil heavy metals. Background technique [0002] Soil is one of the natural resources that human beings rely on for survival. With the development of intensive agricultural production and the acceleration of urbanization, the problem of soil environmental pollution has become increasingly prominent. Heavy metals enter the soil through sewage irrigation and atmospheric deposition. Soil heavy metals are not easy to degrade and migrate. Not only do they enter the human body through the food chain, endangering human health, but excessive soil heavy metals will further pollute the groundwater environment, the ocean, and even the entire ecosystem. Therefore, how to more accurately grasp the spatial distribution of heavy metals in soil is of great significance for the prev...

AI Technical Summary

Problems solved by technology

[0004] However, geostatistical interpolation is based on spatial autocorrelation. Due to the insufficient utilization of auxiliary data and the high correlation with data, the interpolation effect of the same interpolation method in different regions is quite different, which is not conducive to popularization and application; The application of neural networks and support vector machines in spatial interpolation often has overfitting problems, resulting in limited generalization capabilities

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