Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches

Tikhamarine, Yazid; Malik, Anurag; Kumar, Anil; Souag-Gamane, Doudja; Kişi, Özgür

doi:10.1080/02626667.2019.1678750

Cited by 112 publications

(44 citation statements)

References 58 publications

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“…The potential of the MM-ANN, MARS, MGGP, M5Tree, and SVM techniques for simulating the EP were assessed using Nash-Sutcliffe Efficiency (NSE), Willmott's Index of agreement (WI), Legate and McCabe's Index (LM), Root Mean Squared Error (RMSE), (Sharafati, Tafarojnoruz, Shourian, & Yaseen, 2019;Sharafati, Yasa, & Azamathulla, 2018;Tikhamarine, Malik, Kumar, Souag-Gamane, & Kisi, 2019), and Mean Absolute Percent Error (MAPE), Standard Deviation (SD) and Correlation Coefficient (CC) (Malik, Kumar, & Singh, 2019;Singh, Pal, & Singh, 2010). The mentioned performance criteria can be expressed as:…”

Section: Model Performance Evaluation Indicatorsmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

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Section: Model Performance Evaluation Indicatorsmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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“…It enthused their basic concept from the behaviors of bird flocks. The PSO could be used in different fields of optimization, such as multiple-objective optimization, nonlinear and stochastic problems (Malik et al, 2020d;Tikhamarine et al, 2019Tikhamarine et al, , 2020. The working assembly of PSO could be summarized in the following steps:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…Numerous DL approaches viz. Convolutional Neural Network (CNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU) and Recurrent Neural network (RNN) have been implemented in different fields of time series forecasting including rainfall (Hu et al, 2018), air quality (Du et al, 2018), stream flow (Damavandi et al, 2019) and evapotranspiration (Tikhamarine et al, 2019). Liu et al (2016) used a CNN approach to detect three types of climate extreme, namely the river system dynamics, tropical cyclones, and weather fronts independently.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning for Week-Ahead Evapotranspiration Forecasting

Ahmed

Deo

Feng

et al. 2021

Preprint

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Reference evapotranspiration (ET) is an integral hydrological factor in soil-plant-atmospheric water balance studies and the management of drought events. This paper proposes a new hybrid-deep learning (DL) approach, combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) along with Ant Colony Optimization (ACO), for a multi-step (week 1 to 4) daily-ET forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based datasets from Scientific Information for Landowners (SILO) and synoptic-scale climate indices (CI). To develop a vigorous CNN-GRU model, a feature selection stage entails the Ant Colony Optimization (ACO) method implemented to improve the ET forecast model for the three selected sites in Australian Murray Darling Basin. The results demonstrate excellent forecasting capability of the hybrid CNN-GRU model against the counterpart benchmark models, evidenced by a relatively small mean absolute error and high efficiency. Overall, this study shows that the proposed hybrid CNN-GRU model successfully apprehends the complex and non-linear relationships between predictor variables and the daily ET.

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Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches

Cited by 112 publications

References 58 publications

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Hybrid Deep Learning for Week-Ahead Evapotranspiration Forecasting

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