Soil volumetric water content prediction using unique hybrid deep learning algorithm

• Published in: Neural computing & applications Vol. 36; no. 26; pp. 16503 - 16525
• Main Authors: Nath, Koustav, Nayak, P. C., Kasiviswanathan, K. S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Big Data; Boundary conditions; Complexity; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Data processing; Deep learning; Drought; Flood management; Flood predictions; Hydrologic cycle; Hydrology; Image Processing and Computer Vision; Machine learning; Moisture content; Neural networks; Original Article; Physics; Precipitation; Probability and Statistics in Computer Science; Soil water; Support vector machines; Time series; Water resources management; Deep learning; CNN-LSTM model; Recurrent neural network; Recursive feature elimination; Soil moisture
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09991-6

Soil volumetric water content (VWC) is one of the key factors in hydrological cycles and responsible for inducing droughts and floods. Therefore, the precise prediction of VWC is crucial for the effective management of water resources. However, the complexity in structural characteristics and interaction with several other external meteorological factors cause difficulty in establishing a mathematical model which can predict soil VWC accurately. This study demonstrates the applicability of Convolution Neural Network-Long short-term memory (CNN-LSTM) hybrid model to predict soil VWC (%), concentrating specifically on optimizing the predictors combination using recursive feature elimination (RFE) which results in a more interpretable model with less complexity. The model was developed using the data collected from Benton County of Washington, USA, and generalization capacity of the model was tested in other counties of Washington. To verify the improved prediction ability of the proposed model, the results were compared with the established CNN, LSTM and MLR models. The results reflected that the proposed CNN-LSTM model predicted better than the individual CNN, LSTM and MLR models for the training site as well as for the five testing sites, proving its good generalization capacity.