2026· Proceedings of the 1st International Conference on Smart System Design, Application and Mechatronics· pp. 367-372· 0 citations· 9 references
Abstract
: Soil moisture is a key indicator of land surface hydrological processes and plays an essential role in agricultural irrigation management and ecological monitoring. However, traditional monitoring approaches mainly rely on fixed sensors and lack the capability to predict future soil moisture dynamics. To address this issue, this study proposes a soil moisture time series prediction method that integrates a sliding window strategy with a Long Short-Term Memory (LSTM) network. Daily soil moisture data from a single observation station are preprocessed through data cleaning and min – max normalization, and then converted into supervised learning samples using a 14-day sliding window. The LSTM model is constructed to capture nonlinear temporal dependencies in the time series, and its performance is evaluated using RMSE and MAE. Experimental results show that the proposed model achieves an RMSE of 0.0476 and an MAE of 0.0369 on the validation set, demonstrating satisfactory prediction accuracy and stability.
The proposed method performed better than the conventional LSTM algorithm in all forecasting scenarios and showed robust performance even at a 7-day forecasting lead time, showing promise for applications in short-range soil moisture prediction and environmental monitoring studies.
Saeed Samadianfard, E. Khajeh, Neda Beirami et al.· Applied Water Science· 0 citations
A hybrid forecasting model that fuses eXtreme Gradient Boosting for spatial feature importance evaluation with Long Short-Term Memory (LSTM) networks for sequential load prediction is proposed that provides a robust tool for proactive nutrient runoff management in data-sparse agricultural contexts.
Sun-Nan Meng, Sheng-Jun Jin, Hao Wang et al.· International Conference on...· 0 citations
A hybrid rainfall prediction framework which integrates LSTM network with an interval Type-2 fuzzy logic system for one day ahead rainfall prediction and results indicate that the integration of fuzzy layer improves the predictive accuracy.
Meena Pargaei, Vivek Goswami· Theoretical and Applied Clim...· 0 citations
Accurately predicting soil moisture conditions and determining the optimal sowing timing during the spring sowing period play a crucial role in ensuring high and stable grain yields. To address the limitations of existing soil moisture prediction models, i.e., the complexity and parameterization challenges of hydrologi...
Yanhua Wang, Yu-Ying Bai, Fengqian Cui et al.· Water· 0 citations
The increasing availability of agricultural timeseries data enabled more accurate and data-driven crop yield prediction. However, raw meteorological, soil, and vegetation datasets often fail to capture complex temporal dependencies essential for robust forecasting. This study proposes a structured feature engineering...
K. Lata, A. Kumar, P. Sharma et al.· BIO Web of Conferences· 0 citations
Soil moisture is an important variable influencing agricultural productivity, hydrological processes, and land management, particularly in high-rainfall regions such as the North Eastern Hill (NEH) States of India. Although conventional soil moisture measurement techniques provide reliable observations, they are time-c...
Pranjal Dubey, G. T. Patle, Vinay Kumar Gautam· Journal of Agricultural Engi...· 0 citations
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