Forecasting Soil Moisture in Caragana Shrubland Using Wavelet Analysis and NARX Neural Network
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Abstract
It is important for sustainable use of soil water resource and high-quality development to forecast the soil moisture in forestland of water-limited regions. There are some soil water models. However, there is not the best model to forecast the change of soil moisture in the caragana shrubland. In this paper, the plant water relationship has been investigated at the same time for a long term in the caragana shrubland of semiarid region of the Loess Plateau of China. The data of soil moisture was divided and then NARX neural network was used to build model I and model II. For model I, low frequency component was the input variable, and for model II, low frequency component and high frequency component were predicted. The results showed the average relative error for model I is 3.5% and for model II is 0.3%. The average relative error of predicted soil moisture in 100 cm layer using model II is 0.8%, then soil water content in the 40 cm and 200 cm soil depth is selected and the forecast errors are 4.9% and 0.4%. The results showed that using model II to predict soil water is well Predicting soil water using model II will be important for sustainable use of soil water resource and high-quality development.
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Liu HB, Wu W, Wei CF. Study of soil water forecast with neural network. J Soil Water Conserv. 2003;17:59-62. Available from: http://stbcxb.cnjournals.com/stbcxben/article/abstract/200305221
Zhao L, Shui P, Jiang F, Qiu H, Ren S, Li Y, et al. Using monitor data of surface soil to predict whole crop-root zone soil water content with PSO-LSSVM, GRNN, and WNN. Earth Sci Inform. 2014;7:59–68. Available from: https://link.springer.com/article/10.1007/s12145-013-0130-6
Pisoni E, Farina M, Carnevale C, Piroddi L. Forecasting peak air pollution levels using NARX models. Eng Appl Artif Intell. 2009;22:593-602. Available from: https://doi.org/10.1016/j.engappai.2009.04.002
Cai L, Ma SY, Cai HT, Zhou YL, Liu R. Prediction of SYM-H index by NARX neural network from IMF and solar wind data. Sci China Ser E Technol Sci. 2009;52:2877-2885. Available from: https://link.springer.com/article/10.1007/s11431-009-0296-9
Liu HB, Xie DT, Wu W. Soil water content forecasting by ANN and SVM hybrid architecture. Environ Monit Assess. 2008;143:187-193. Available from: https://link.springer.com/article/10.1007/s10661-007-9967-9
Guo QC, He ZF. Forecast model of soil water content based on artificial neural network. J Shanxi Agric Sci. 2012;40:892-895.
Kseneman M, Gleich D, Božidar P. Soil-moisture estimation from TerraSAR-X data using neural networks. Mach Vis Appl. 2012;23:937-952. Available from: https://link.springer.com/article/10.1007/s00138-011-0375-3
Xu TB, Ma GW, Huang WB, et al. BP network prediction model and its application in annual runoff forecasting based on wavelet analysis. Water Resour Power. 2012;30:17-19.
Guo Z. Soil hydrology process and sustainable use of soil water resources in desert regions. Water. 2021;13(17):2377. Available from: https://doi.org/10.3390/w13172377
Lu ZJ, Zhu L, Pei HP. The model of chlorophy II-a concentration forecast in the West Lake based on wavelet analysis and BP neural networks. Chinese J Ecol. 2008;28:4965-4973.
FAO/UNESCO. Soil map of the world, revised legend. Rome: FAO/UNESCO; 1988.
Jiang X, Liu H. Radial basis function networks based on wavelet analysis for the annual flow forecast. J Appl Sci. 2004;22:411-414.
Zhang DF. MATLAB wavelet analysis. 2nd ed. Beijing: Mechanical Industry Press; 2012.
Yang WZ, Shao MA. Study on soil moisture in the Loess Plateau. Beijing: Science Press; 2000.
Zhou W, Gui L, Zhou L. High-level technology of MATLAB wavelet analysis. Xi’an: Xidian University Press; 2006. p. 51-63.
Shi Y, Han LQ, Lian XQ. Neural network design and instance analysis. Beijing: Beijing University of Posts and Telecommunications Press; 2009.