Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring

作者：Jie Song, Yujun Yi · 发表于：Water Resources Research · 年份：2025 · DOI：10.1029/2024wr038271 · 被引用次数：9 · 研究领域：Coastal wetland ecosystem dynamics

Abstract Estuarine wetlands, formed by the interaction of fluvial and tidal processes, exhibit complex spatiotemporal variations in soil moisture and salinity. Predicting soil moisture and salinity in estuarine wetlands is key for ecosystem management and assessing environmental impacts, while traditional methods have limitations in resolution and complexity. The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. To address these challenges and improve our ability to predict and manage wetland soil properties, this study employs an in situ Internet of Things (IoT)‐based monitoring network and a interpretable, cascaded machine learning model to predict these critical soil parameters. The IoT platform facilitates real‐time and longitudinal tracking of soil volumetric moisture content, salinity, and groundwater depth in the Yellow River Delta salt marsh wetlands, and the high‐fidelity monitoring data are used to build a two‐stage machine learning model. Artificial Neural Networks, Support Vector Machines, Random Forests (RF), and Gradient Boosting Decision Trees (GBDT) were used to develop the soil moisture and salinity prediction models. The cascaded framework, in combination with a moisture and a salinity sub‐model, which inspired by soil water and salt transport processes, was found to be an effective approach for capturing moisture‐salinity dynamics. The Gradient Boosting Decision Tree (GBDT) algorithm ...