Deep learning cellular automaton model-based soil moisture content prediction method

Abstract

The invention relates to a deep learning cellular automaton model-based soil moisture content prediction method. According to the method, a machine learning and geographical phenomenon simulation are used in combination; different time-space prediction aspects of soil moisture content are improved; a soil moisture content prediction function local optimal solution can be obtained by means of deep learning; and a quantitative test is performed on the generalization ability of the model through using a model inspection mechanism, and a self-improvement mechanism of a cellular automaton is put forward, and therefore, the robustness of the model can be ensured better. The hybrid technology provided by the invention is expected to provide technical support for soil moisture content real-time monitoring in complex regions. With the prediction method adopted, prediction cost of the soil moisture content can be reduced, and prediction accuracy of the soil moisture content can be significantly improved. The prediction method has a wide industrial application prospect.

Description

technical field [0001] The invention relates to a soil water content prediction method based on a deep learning cellular automata model, and belongs to the technical field of soil surface water content prediction. Background technique [0002] Soil water content is the main source of water absorption by surface vegetation, which directly affects the growth of ecological vegetation. Accurately estimating soil surface water content has become a hot issue in agricultural water resource monitoring. The determination methods of soil water content mainly include contact direct measurement and non-contact remote sensing monitoring. Obtaining soil water content by means of remote sensing inversion has the characteristics of large range and high time resolution. The effect of this method is not ideal in areas with high vegetation coverage, and if the sensor is affected by external factors, it cannot retrieve the soil moisture content in real time. In addition to remote sensing mon...

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Problems solved by technology

[0009] In view of the above technical problems, the technical problem to be solved by the present invention is to provide a soil moisture prediction method based on the deep learning cellular automata model, obtain the cellular state transition rules based on the deep learning network, and combine the generalization of the cellular automata Quantitative evaluation of capacity can solve the problems of low data management efficiency and difficult parallel scheduling of existing visual field algorithms in a distributed parallel computing environment, effectively improving the accuracy of soil moisture prediction

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