Abstract
Soil moisture prediction with high quality has important guiding effect on agricultural production. Aiming at the problem that the time series of soil moisture is non-stationary, a prediction method which combine wavelet transform and improved Back Propagation Neural Network (BPNN) is proposed in this paper. First obtained several relatively stable data sequence with different scales by decompose the original time series using the discrete wavelet transform, then use BPNN to predict each sequence respectively, finally reconstruct to obtain final prediction result. Aiming at the problem that BPNN has slow converging speed and easy to fall into the local optimal solution, proposed an optimization method by adjusting the momentum factor and learning rate adaptively in the learning process. The experiment has been carried out in KenLi town, the research region of “BoHai Barn” in ShanDong province, using data from 10 observation stations. We use 3 prediction methods respectively to predict the original time series, experimental results explicit that the proposed method has higher prediction accuracy (compared with the other two methods, increased by 9.5% and 31% respectively) and fewer iterations (compared with the other two methods, increased by 70% and 77% respectively).
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Yang, X., Jia, S., Zhang, C. (2020). A Prediction Method for Soil Moisture Time Series. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_49
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DOI: https://doi.org/10.1007/978-3-030-62463-7_49
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