GLUE analysis of meteorological-based crop coefficient predictions to derive the explicit equation

Elbeltagi, Ahmed; Seifi, Akram; Ehteram, Mohammad; Zerouali, Bilel; Vishwakarma, Dinesh Kumar; Pandey, Kusum

doi:10.1007/s00521-023-08466-4

Cited by 8 publications

(1 citation statement)

References 68 publications

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“…Also, these models are flexible enough to predict hydrological problems with high efficiency [ [31] , [32] , [33] ]. Machine learning and artificial intelligence models have become very popular in recent decade [ [34] , [35] , [36] , [37] , [38] ]. Forecasting of the stream discharge various models such as multiple-linear regression (MLR) [ 2 , [39] , [40] , [41] , [42] ], rating curve [ [43] , [44] , [45] , [46] , [47] ], wavelet-based MLR (WMLR) [ 48 , 49 ], support vector machine (SVM) [ 39 , 44 , [50] , [51] , [52] , [53] ], artificial neural network (ANN) [ 45 , [53] , [54] , [55] , [56] , [57] ], wavelet-based artificial neural network (WANN) [ 2 , 39 , 58 ], adaptive neuro-fuzzy inference system (ANFIS) [ [59] , [60] , [61] ], wavelet-based support vector machine (WSVM) [ 39 , 62 ], wavelet–bootstrap–ANN (WBANN) [ 48 , 63 ], M5-model trees [ 46 , 64 ], random forest (RF) [ 65 ], ARIMA [ 65 , 66 ], gene expression programming (GEP) [ 32 , 67 , 68 ], genetic algorithm (GA) [ 3 , 33 , 69 ], genetic programming (GP) [ 32 ], Bagged M5P [ 65 ], integrating long-short-term memory (LSTM) [ 69 , 70 ], wavelet–bootstrap–multiple linear regression (WBMLR) [ 48 ], Fuzzy logic and f...…”

Section: Introductionmentioning

confidence: 99%

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

Vishwakarma

Kuriqi

Abed

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

Vishwakarma

Kuriqi

Abed

et al. 2023

Heliyon

Self Cite

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Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions

Vishwakarma,

Kumar,

Yadav

et al. 2024

Pure Appl. Geophys.

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Pan evaporation modelling and forecasting is needed to provide timely, continuously, and valuable water information to support water management. In this study we predict weekly pan evaporation using six machine learning approaches.The proposed models were:the multiple linear regression (MLR), the multiple nonlinear least-squares regression (MNLSR), the multivariate adaptive regression splines (MARS), the random forest regression (RF), the CatBoostand the M5 Model Tree (M5Tree).The algorithms were developed using data collected at Crop Research Center (CRC), Pantnagar-Uttarakhand, India. Stepwise regression was used as input variable selection algorithm for selecting the best relevant input from several meteorological variables. For models development we split the data into two subsets: the first three years reserved for model calibration i.e., validation, and the fourth-year data was used validation purpose.The rootmean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Willmott index of agreement (WI), Pearson correlation coefficient (PCC), coefficient of determination (R 2 ), and mean absolute error (MAE) were used to assess the effectiveness of weekly pan evaporation estimation models. CatBoost model based on four predictors was the most accurate at Pantnagar station, with NSE, WI, MAE, RMSE, PCC and R 2 ranging from 0.9838 to 0.9877, 0.9960 to 0.9968, 0.1991 to 0.2078, 0.3643 to 0.3650 to 0.9955, 0.9925 and 0.9851 to 0.9911, respectively. Random Forest model has scored the second rank in terms of accuracy and effectiveness for mapping weekly pan evaporation followed by MARS, MNLSR, MLR, and M5Tree.

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Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India

Satpathi,

Danodia,

Nain

et al. 2024

Theor Appl Climatol

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GLUE analysis of meteorological-based crop coefficient predictions to derive the explicit equation

Cited by 8 publications

References 68 publications

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions

Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India

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