Version 1
: Received: 1 December 2022 / Approved: 8 December 2022 / Online: 8 December 2022 (02:28:04 CET)
How to cite:
Huang, X.; Wu, J.; Chu, J. Soil Moisture Prediction Based on the LSTM Neural Network Integrating Particle Swarm Optimization. Preprints2022, 2022120141. https://doi.org/10.20944/preprints202212.0141.v1
Huang, X.; Wu, J.; Chu, J. Soil Moisture Prediction Based on the LSTM Neural Network Integrating Particle Swarm Optimization. Preprints 2022, 2022120141. https://doi.org/10.20944/preprints202212.0141.v1
Huang, X.; Wu, J.; Chu, J. Soil Moisture Prediction Based on the LSTM Neural Network Integrating Particle Swarm Optimization. Preprints2022, 2022120141. https://doi.org/10.20944/preprints202212.0141.v1
APA Style
Huang, X., Wu, J., & Chu, J. (2022). Soil Moisture Prediction Based on the LSTM Neural Network Integrating Particle Swarm Optimization. Preprints. https://doi.org/10.20944/preprints202212.0141.v1
Chicago/Turabian Style
Huang, X., Jianlong Wu and Jizheng Chu. 2022 "Soil Moisture Prediction Based on the LSTM Neural Network Integrating Particle Swarm Optimization" Preprints. https://doi.org/10.20944/preprints202212.0141.v1
Abstract
Soil moisture is an important factor affecting the plant growth. For a long time, the convenience, timeliness and accuracy of soil moisture monitoring have been limited due to the backward of observation methods and equipment. Therefore, the quantitative prediction of soil moisture has become a difficult problem. Aiming at the problems of high erection cost, easily damaged sensors and low measurement accuracy of the existing fixed sensor soil moisture monitoring system, a soil moisture prediction model based on the long short term memory neural network (LSTM) integrating the particle swarm optimization (PSO) (PSO-LSTM) is designed and implemented. The hyperparameters of the LSTM network can be obtained based on the excellent global search ability of the PSO algorithm. According to the meteorological data and soil moisture data of Haidian Park in 2019, the long short term memory(LSTM) neural network based prediction model is constructed with input vectors of surface temperature, average temperature, evaporation, sunshine hours, precipitation and average wind speed, and the output vector of soil relative humidity. The results show that compared with the back propagation(BP) neural network, the Elman neural network and the LSTM neural network, the proposed PSO-LSTM model has higher prediction performance.
Keywords
Soil moisture prediction; LSTM; PSO
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
