Evolutionary algorithm for reference evapotranspiration analysis

Jović, Srđan; Nedeljković, Blagoje; Golubović, Zoran; Kostić, Nikola

doi:10.1016/j.compag.2018.04.003

Cited by 49 publications

(21 citation statements)

References 14 publications

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“…Evapotranspiration (ET), as suggested by the term itself, is the combination of evaporation of water from land and plant surfaces and transpiration from vegetation through the leaves' stomata [3]. ET is a natural event that affects the hydrological cycle, which is believed to be highly complex that involves several nonlinear processes [4]. There are numerous factors that govern the rate of evapotranspiration and these include temperature, solar radiation, air humidity, and wind speed [5].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

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Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

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“…The potential of the GEP model for the estimation of ET o was analyzed by Jovic, Nedeljkovic, Golubovic, and Kostic (2018). The models were built based on diverse meteorological variables including minimum and maximum temperature, vapor pressure, wind speed, sunshine hours and relative humidity.…”

Section: State Of the Art: Evolutionary Computing (Ec) Models For Et mentioning

confidence: 99%

Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

Wang

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Shahid

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ET o) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ET o is a major concern in hydrology. ET o can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ET o. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ET o. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ET o. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ET o in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ET o using EC.

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“…Shamshirband [37] found that the cuckoo search algorithm optimized ANN and ANFIS had an RMSE value of 0.330 and 0.265 mm d − 1 in the testing stage at Serbia, respectively. Another case study in Serbia showed that the genetic programming models coupling with GA generated RMSE at 0.649-1.188 and 0.657-1.193 mm d − 1 in the testing stage and the validation, respectively [38]. In addition, a hybrid model (GA-SVM) conducted in the Pailugou Watershed, Northwest China, showed that the RMSE values ranged at 0.138-0.424 mm d − 1 [21,36].…”

Section: Advances In Meteorologymentioning

confidence: 99%

Coupling a Bat Algorithm with XGBoost to Estimate Reference Evapotranspiration in the Arid and Semiarid Regions of China

Han

Zhai

et al. 2019

Advances in Meteorology

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Accurate estimation of reference evapotranspiration (ETo) is key to agricultural irrigation scheduling and water resources management in arid and semiarid areas. This study evaluates the capability of coupling a Bat algorithm with the XGBoost method (i.e., the BAXGB model) for estimating monthly ETo in the arid and semiarid regions of China. Meteorological data from three stations (Datong, Yinchuan, and Taiyuan) during 1991–2015 were used to build the BAXGB model, the multivariate adaptive regression splines (MARS), and the gaussian process regression (GPR) model. Six input combinations with different sets of meteorological parameters were applied for model training and testing, which included mean air temperature (Tmean), maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed (U), relative humidity (RH), and solar radiation (Rs) or extraterrestrial radiation (Ra, MJ m−2·d−1). The results indicated that BAXGB models (RMSE = 0.114–0.412 mm·d−1, MAE = 0.087–0.302 mm·d−1, and R2 = 0.937–0.996) were more accurate than either MARS (RMSE = 0.146–0.512 mm·d−1, MAE = 0.112–0.37 mm·d−1, and R2 = 0.935–0.994) or GPR (RMSE = 0.289–0.714 mm·d−1, MAE = 0.197–0.564 mm·d−1, and R2 = 0.817–0.980) model for estimating ETo. Findings of this study would be helpful for agricultural irrigation scheduling in the arid and semiarid regions and may be used as reference in other regions where accurate models for improving local water management are needed.

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Evolutionary algorithm for reference evapotranspiration analysis

Cited by 49 publications

References 14 publications

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

Coupling a Bat Algorithm with XGBoost to Estimate Reference Evapotranspiration in the Arid and Semiarid Regions of China

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