Modelling monthly reference evapotranspiration estimation using machine learning approach in data-scarce North Western Himalaya region (Almora), Uttarakhand

Kumar, Utkarsh

doi:10.1007/s12040-023-02138-6

Cited by 2 publications

(1 citation statement)

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“…Since estimation is very region-specific, the EvatCrop was compared with the other relevant models reported in the literature for the Indian context. Kumar (2023) conducted an experiment with three machine learning models (Random Forest, gradient boosted trees, support vector machines) and one deep learning model (long-short term memory) for estimation in data-scare conditions for Uttarakhand, India, and reported that the Random Forest and gradient boosted trees achieved the highest accuracy with an average value of 0.920 in the testing phase. Compared to this, EvatCrop recorded the highest average value of 0.982 for the test set.…”

Section: Discussionmentioning

confidence: 99%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

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Section: Discussionmentioning

confidence: 99%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation

Kumar,

Agrawal,

Adamala

et al. 2024

Water

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The potential of generalized deep learning models developed for crop water estimation was examined in the current study. This study was conducted in a semiarid region of India, i.e., Karnataka, with daily climatic data (maximum and minimum air temperatures, maximum and minimum relative humidity, wind speed, sunshine hours, and rainfall) of 44 years (1976–2020) for twelve locations. The Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Random Forest (RF) are three ensemble deep learning models that were developed using all of the climatic data from a single location (Bengaluru) from January 1976 to December 2017 and then immediately applied at eleven different locations (Ballari, Chikmaglur, Chitradurga, Devnagiri, Dharwad, Gadag, Haveri, Koppal, Mandya, Shivmoga, and Tumkuru) without the need for any local calibration. For the test period of January 2018–June 2020, the model’s capacity to estimate the numerical values of crop water requirement (Penman-Monteith (P-M) ETo values) was assessed. The developed ensemble deep learning models were evaluated using the performance criteria of mean absolute error (MAE), average absolute relative error (AARE), coefficient of correlation (r), noise to signal ratio (NS), Nash–Sutcliffe efficiency (ɳ), and weighted standard error of estimate (WSEE). The results indicated that the WSEE values of RF, GB, and XGBoost models for each location were smaller than 1 mm per day, and the model’s effectiveness varied from 96% to 99% across various locations. While all of the deep learning models performed better with respect to the P-M ETo approach, the XGBoost model was able to estimate ETo with greater accuracy than the GB and RF models. The XGBoost model’s strong performance was also indicated by the decreased noise-to-signal ratio. Thus, in this study, a generalized mathematical model for short-term ETo estimates is developed using ensemble deep learning techniques. Because of this type of model’s accuracy in calculating crop water requirements and its ability for generalization, it can be effortlessly integrated with a real-time water management system or an autonomous weather station at the regional level.

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Modelling monthly reference evapotranspiration estimation using machine learning approach in data-scarce North Western Himalaya region (Almora), Uttarakhand

Cited by 2 publications

References 50 publications

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation

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