Soil moisture prediction method based on machine learning algorithm

Abstract

The invention belongs to the technical field of soil detection, and discloses a soil moisture prediction method based on a machine learning algorithm. The method comprises the following steps: establishing a soil moisture data and prediction factor database; soil moisture simulation prediction and time-space characteristic detection: establishing a soil moisture prediction model in an R language environment by adopting a support vector machine, a random forest and a BP neural network algorithm; performing comprehensive evaluation and space-time characteristic analysis on the soil moisture by adopting a related statistical analysis method; analyzing the prediction model through cross validation and other methods, and evaluating the precision of the method through precision indexes; and determining the importance of the soil moisture predictor. According to the method, prediction factors of terrain, weather, soil, vegetation and the like are selected in combination with local actual conditions, and a novel algorithm-machine learning algorithm is introduced to construct a model to predict the soil moisture, so that accurate prediction of the local soil moisture is realized.

Description

technical field [0001] The invention belongs to the technical field of soil detection, and in particular relates to a soil moisture prediction method based on a machine learning algorithm. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] As the link connecting groundwater, surface water and biological water, soil water is an important carrier for the conversion and transmission of substances in nature; soil moisture has a direct and far-reaching impact on soil ecological environment and agricultural sustainable development. The evaluation of soil moisture content based on various soil-forming factors plays an important role in agricultural science and is also a key step in the development of scientific farming and precision agriculture. In the planting and management of field crops, suitable soil moisture is an important factor to ensure the quality and yield of crops and prevent agricultural drought;...

AI Technical Summary

Problems solved by technology

[0010] Therefore, the limitations of the existing technology (1) the data source itself: soil moisture monitoring is initially established using the data of observation stations. This type of method has been researched for a long time and is relatively mature. my country has also compiled the corresponding soil water shortage level standards

Most of the remote sensing moisture monitoring indexes do not take into account the precipitation factor, resulting in uncertainty in the moisture monitoring results, which cannot better reflect the change and development of soil moisture.

(3) Limitations of soil moisture prediction models: Although the simulation prediction of soil moisture has gone through a long period of development, and the results are relatively rich, the prediction accuracy of these models is relatively satisfactory when the preconditions are met, but in the actual situation There are still some problems in the promotion and use of

[0013] (1) Limitations of moisture monitoring indices: most remote sensing moisture monitoring indices do not take into account precipitation factors, resulting in uncertainty in moisture monitoring results, which cannot better reflect changes and developments in soil moisture

[0014] (2) Limitations of soil moisture prediction models: Considering regional differences, the workload of obtaining various environmental parameters is huge and the randomness is difficult to predict, which seriously limits the universality of the model; some models themselves are too complex, Due to the lack of parameters, the prediction effect will be significantly reduced; for many years, soil moisture prediction and drought analysis are mainly based on experience, lacking accuracy, scientificity and timeliness

Among them, the parameters of the empirical model are simple and easy to obtain, but it consumes a lot of time and manpower; the use of multi-source data such as remote sensing has accelerated the development of large-scale monitoring and prediction of soil moisture, but the parameters of the model are diverse and complex and are affected by factors such as soil depth and surface cover. Greater impact

Images