Modelling reference evapotranspiration using Gene Expression Programming and Artificial Neural Network at Pantnagar, India
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TL;DR: In this paper , the authors used both gene expression programming (GEP) and artificial neural network (ANN) techniques to model reference evapotranspiration (ET0) using the daily meteorological data of the Pantnagar region, India, from 2010 to 2019.
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About: This article is published in Information Processing in Agriculture. The article was published on 01 May 2022. and is currently open access. The article focuses on the topics: Evapotranspiration & Gene expression programming.
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Citations
A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives
TL;DR: A comprehensive review of the state-of-the-art standalone AI frameworks for ETo prediction can be found in this paper , along with the intriguing developments in the advanced AI space such as the hybrid and ensemble models, evolutionary models and a range of optimization techniques.
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Performance evaluation of soft computing techniques for forecasting daily reference evapotranspiration
Jitendra Rajput,Manjusha Singh,Kishore Lal,Manoj Khanna,A. Sarangi,Jhuma Mukherjee,Shrawan Singh +6 more
TL;DR: In this paper , the best ANN model with sigmoid activation function and the L-BFGS learning algorithm was selected as the best-performing model among 36 ANN models tested in this study.
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Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management.
Niguss Solomon Hailegnaw,Haimanote K. Bayabil,Mulatu Liyew Berihun,Fitsum Teshome,Vakhtang Shelia,Fikadu Getachew +5 more
TL;DR: Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance, and in areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed.
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Predictive Modelling of Reference Evapotranspiration Using Machine Learning Models Coupled with Grey Wolf Optimizer
TL;DR: In this paper , different machine learning (ML) algorithms, namely random forests (RF), extreme gradient boosting (XGB), and light gradient boosting(LGB), were optimized using the naturally inspired grey wolf optimizer (GWO) viz. GWORF, GWOXGB, and GWOLGB.
Performance of Machine Learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons
Nehar Mandal,Kironmala Chanda +1 more
TL;DR: In this article , the potential of six machine learning approaches are examined across different agro climatic zones and cropping seasons for multi-step-ahead potential evapotranspiration (ETO) in India using globally available European Centre for Medium-Range Weather Forecasts (ECMWF) gridded climate reanalysis products (ERA5).
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References
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Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)
TL;DR: In this article, the use of artificial neural network (ANN) models for the estimation of daily ETo has been evaluated in three groups of evaluated methods: temperature and/or relative humidity-based methods (0.385mm-d−1 of root mean square error (RMSE)), solar radiation-based method ( 0.238-mm−d− 1 of RMSE), and methods based on similar requirements to those of PM56 except for the estimated meteorological parameters as inputs.
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Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling.
TL;DR: Wang et al. as mentioned in this paper proposed two artificial intelligence models, random forests (RF) and generalized regression neural networks (GRNN), for daily evapotranspiration (ET 0 ) estimation.
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Evapotranspiration evaluation models based on machine learning algorithms—A comparative study
TL;DR: In this paper, three different evapotranspiration models have been compared in an experimental site in Central Florida, characterized by humid subtropical climate, and four variants of each model were applied, varying the machine learning algorithm: M5P Regression Tree, Bagging, Random Forest and Support Vector Regression.
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Modeling rainfall-runoff process using soft computing techniques
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