Abstract
Rice cultivation demands adequate soil water balance in each growth stage and estimation of evapotranspiration and soil moisture are the most contributing factors for determining this. Evapotranspiration prediction is essential as it indicates rice water demand in advance depending on several environmental conditions and soil moisture prediction helps to judiciously schedule irrigation based on soil water balance. Several environmental parameters are having impact on evapotranspiration and soil moisture prediction. In order to select correlated environmental parameters with evapotranspiration and soil moisture, salp swarm algorithm which is improved using opposition based learning, local search algorithm, and a control parameter called inertia weight (ISSA) is used. Feature Weighted K-Nearest Neighbor is consolidated with ISSA to assess the quality of selected environmental parameters. Evapotranspiration is predicted using individual machine learning models and ensemble learning but individual machine learning models suffers from high bias and variance in prediction and cannot provide the desired prediction accuracy. Boosting outperform all the models with Mean Absolute Error (MAE) [0.10, 0.03, 0.05], Mean Squared Error (MSE) [0.15, 0.09, 0.109], Root Mean Square Error (RMSE) [0.387, 0.300, 0.330], Nash Sutcliffe Efficiency (NSE) [0.959, 0.948, 0.692] and Coefficient of determination (\(R^{2}\)) [0.962, 0.941, 0.697] for evapotranspiration prediction in vegetative, reproductive and ripening growth stages respectively using selected features. Soil moisture is also predicted where Boosting also outperforms other methods with MAE [0.168, 0.131, 0.08], MSE [0.425, 0.142, 0.128], RMSE [0.651, 0.376, 0.357], NSE [0.912, 0.928, 0.698] and \(R^{2}\) [0.918, 0.932, 0.717] in every rice growth stage using selected features.
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Conceptualization: Parijata Majumdar and Sanjoy Mitra. Methodology: Parijata Majumdar. Formal analysis and investigation: Sanjoy Mitra and Parijata Majumdar. Data curation and software: Parijata Majumdar and Diptendu Bhattacharya. Validation: Sanjoy Mitra. Visualization: Diptendu Bhattacharya and Parijata Majumdar. Writing — original draft preparation: Parijata Majumdar and Sanjoy Mitra. Writing — review and editing: Sanjoy Mitra. Supervision: Diptendu Bhattacharya and Sanjoy Mitra. Funding acquisition: not applicable. Resources: Sanjoy Mitra and Diptendu Bhattacharya
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Majumdar, P., Bhattacharya, D. & Mitra, S. Prediction of evapotranspiration and soil moisture in different rice growth stages through improved salp swarm based feature optimization and ensembled machine learning algorithm. Theor Appl Climatol 153, 649–673 (2023). https://doi.org/10.1007/s00704-023-04414-3
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DOI: https://doi.org/10.1007/s00704-023-04414-3