Prediction of evapotranspiration and soil moisture in different rice growth stages through improved salp swarm based feature optimization and ensembled machine learning algorithm

Majumdar, Parijata; Bhattacharya, Diptendu; Mitra, Sourav

doi:10.1007/s00704-023-04414-3

Cited by 10 publications

(2 citation statements)

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“…The ensemble model tends to enhance the predictive accuracy in complex problems and reduce bias in model training [2,29]. The purpose of the implementation of the ensemble approach is to explore the possibilities of accurate ET forecasting with different configurations of the LSTM models.…”

Section: Implementation Of ML Modelsmentioning

confidence: 99%

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IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh

Nauman,

Saeed,

Saidani

et al. 2023

Sensors

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Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature (T), mean humidity (Hm), and maximum wind speed (Ws) are used to forecast evapotranspiration (ET). Long short-term memory (LSTM) and ensemble LSTM with bagged and boosted approaches are implemented and evaluated for their accuracy in forecasting ET values using meteorological data from 2001 to 2023. The results demonstrate that the bagged LSTM approach accurately forecasts ET with limited meteorological conditions in Riyadh, Saudi Arabia, with the coefficient of determination (R2) of 0.94 compared to the boosted LSTM and off-the-shelf LSTM with R2 of 0.91 and 0.77, respectively. The bagged LSTM model is also more efficient with small values of root mean squared error (RMSE) and mean squared error (MSE) of 0.42 and 0.53 compared to the boosted LSTM and off-the-shelf LSTM models.

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Section: Implementation Of ML Modelsmentioning

confidence: 99%

“…Majumdar et al [29] recommended an IoT and ML-oriented approach for the forecasting of ET at each stage of rice growth. For ET prediction, individual ML models and ensemble learning methods are compared.…”

mentioning

confidence: 99%