Thresholds of key disaster-inducing factors and drought simulation in the Xilinguole Grassland

Liu, Xiaoxu; Zhu, Zhongyuan; Liu, Xiaomin; Yu, Miao

doi:10.1016/j.ecoinf.2021.101380

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…The inter‐monthly variability of precipitation is significant, with the main concentration from June to September, accounting for approximately 74.4% of the annual precipitation. The dominant soil type in the area is Chestnut Soil, and the most widespread plant communities are Leymus chinensis (Trinius ex Bunge) Tzvelev and Stipa grandis P. Smirn communities (Kolås et al., 2014; Liu et al., 2021; Lu et al., 2022).…”

Section: Methologymentioning

confidence: 99%

Innovative approach to predict soil moisture using the backpropagation‐Elman neural network

Zhang,

Wei,

Xue

et al. 2024

Agronomy Journal

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Accurate moisture prediction aids in taking early preventive measures to minimize damage caused by severe storms, droughts, and floods. The study collected 24 types of statistical data from the Xilingol Grassland, Inner Mongolia, spanning from 2011 to 2021. By integrating Elman, backpropagation (BP), and soil moisture prediction model (Elman‐BP) neural networks, soil moisture to 2 m was predicted. The soil moisture mechanism model analysis revealed that soil moisture is related to water supply rather than directly correlated with time. Based on these findings, a two‐stage soil moisture prediction model was established. In the first stage, the Elman neural network model predicted future precipitation data based on observed values. In the second stage, soil moisture was predicted using a BP neural network model based on predicted precipitation data. Comparing the mean squared errors of training and prediction data across the three models revealed that the Elman‐BP neural network had superior predictive accuracy. The maximum relative errors for the trained Elman neural network, BP neural network, and Elman‐BP model were 31.35%, 20.98%, and 3.94%. Overall, results showed a link between soil moisture and water supply and that soil moisture decreased with increasing temperatures. Soil moisture at 10‐ and 40‐cm depths was increased with increasing rainfall. Furthermore, a delay exists between the 40‐ and 100‐cm soil depths. Moreover, soil moisture stability increased with depth.

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Section: Methologymentioning

confidence: 99%