| Cite this article: | 左炳昕,查元源.基于机器学习方法的土壤转换函数模型比较[J].灌溉排水学报,0,():-. |
| ZUO Bingxin,ZHA Yuanyuan.基于机器学习方法的土壤转换函数模型比较[J].灌溉排水学报,0,():-. |
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| DOI： |
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| Comparison of Pedotransfer Function Models Based on Machine Learning Methods |
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ZUO Bingxin1, ZHA Yuanyuan2
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1.The School of Water Resources and Hydropower Engineering, Wuhan University;2.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University
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| Abstract: |
| 【Background】The accurate description and prediction of soil moisture state and movement process requires accurate soil hydraulic parameters. The PedoTransfer Functions (PTFs) can easily obtain soil hydraulic parameters with less time and effort. For different modeling methods, there are differences in model prediction accuracy and operating efficiency. 【Objective】In order to explore the advantages and disadvantages of different machine learning methods to establish pedotransfer function models under uniform conditions, 【Method】this study used the UNSODA database with global soil hydraulic properties to compare and analyze the prediction accuracy of artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN) on the parameters of van Genuchten model and the saturated hydraulic conductivity. All the model was trained and tested with the same soil samples, using sand, silt, clay percentage and bulk density as the input information to predict the parameters of van Genuchten model and the saturated hydraulic conductivity. We used the measured water content-matrix potential data and the saturated hydraulic conductivity of soil samples for the evaluating the performance of each model from the perspective of soil texture. 【Result】The results showed that the SVM model has the best predictive performance on the sample, followed by ANN, and the KNN model has a relatively lower predictive performance due to edge effects. In addition, this study also evaluated the operating efficiency of each model. The results show that when training the model, the ANN model takes the longest time, while the KNN model takes the shortest time. However, while during forecasting, the ANN model takes the shortest time, while the KNN model takes the longest time. In addition, we compared the popular used Rosetta model, which is a pedotransfer function model using a single hidden layer neural network. The results showed that the multi hidden layer neural network used in this study can improve the prediction accuracy of the model. In addition, using a more appropriate modeling method and increasing the amount of information of input data will help to improve the prediction accuracy of the model as well.【Conclusion】In this study, it is recommended to use the SVM model when building pedotransfer function models on small datasets since it can balance prediction accuracy and operational efficiency. However, the prediction efficiency of the ANN model is higher, and on large database, the ANN model can use mini-batch method to training the model without increasing or a small amount of calculation, so this research recommends that a more reasonable selection of modeling methods should be comprehensively considered in terms of calculation cost and prediction accuracy on large database. |
| Key words: pedotransfer function; machine learning; artificial neural network; support vector machine; K-nearest neighbor |
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