Predicting reference evapotranspiration in semi-arid-region by regression- based machine learning methods using limited climatic inputs

Sammen, Saad Sh.; Kişi, Özgür; Al‐Janabi, Ahmed Mohammed Sami; Elbeltagi, Ahmed

doi:10.21203/rs.3.rs-2600302/v1

Cited by 2 publications

References 73 publications

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Estimation of Daily Reference Evapotranspiration using Machine Learning and Deep Learning Techniques with Sparse Meteorological Data

Nayak,

Sarangi,

Pradhan

et al. 2024

Preprint

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Accurate estimation of evapotranspiration is very crucial for enhancing the real time irrigation scheduling and decision making in water resources planning. Traditionally, empirical methods are used to calculate the reference evapotranspiration using available meteorological data. However, in many areas, such data is limited or unavailable for ETo estimation. Hence, this study aims to explore data-driven models like machine learning (ML) and deep learning (DL) for estimating ETo with minimal meteorological data. In this study, five ML models, including linear regression (LR), random forest (RF), support vector regression (SVR), XGBoost, KNN regression, and two deep learning methods such as feedforward neural networks and long-term short-term memory (LSTM), were used to estimate the reference evapotranspiration (ETo) over the Phulnahara canal command area using various combinations of meteorological parameters. The results of these models were compared with the Penman-Monteith-based ETo. The Penman-Monteith-based ETo exhibits a significant positive correlation with sunshine hour and maximum temperature, displaying correlation coefficients of 0.8 and 0.6, respectively, while RHmin and RHmax demonstrate a negative correlation. The findings revealed that when all climate data is available, the coefficient of determination (R2) rises to 0.98. However, when data is limited, it drops to 0.78. The SVR model outperformed other ML models with all input combinations. However, KNN emerged as the most reliable model for estimating ETo with input data of maximum and minimum temperature. Interestingly, we found that even using just three parameters (temperature, wind speed, and relative humidity) or two-parameter combinations (temperature and relative humidity or temperature and wind speed) can yield promising results in ETo estimation. The findings of this study offer valuable insights for estimating ETo in regions with limited climate data, which is crucial for effective agricultural water management.

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Estimation of Daily Reference Evapotranspiration using Machine Learning and Deep Learning Techniques with Sparse Meteorological Data

Nayak,

Sarangi,

Pradhan

et al. 2024

Preprint

View full text Add to dashboard Cite

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Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition

Rajput

Singh

Lal

et al. 2023

Water Science &Amp; Technology

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Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ET0) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman–Monteith (PM56) model estimated daily ET0. Results revealed that the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ET0 modeling during the model training period. While, in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ET0 in semi-arid climatic conditions.

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Predicting reference evapotranspiration in semi-arid-region by regression- based machine learning methods using limited climatic inputs

Cited by 2 publications

References 73 publications

Estimation of Daily Reference Evapotranspiration using Machine Learning and Deep Learning Techniques with Sparse Meteorological Data

Estimation of Daily Reference Evapotranspiration using Machine Learning and Deep Learning Techniques with Sparse Meteorological Data

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition

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