New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa

Traoré, Seydou; Güven, Aytaç

doi:10.1007/s00271-011-0288-y

Cited by 49 publications

(9 citation statements)

References 21 publications

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“…GEP14 contained both RH and n and gave the best performance for R 2 and RMSE (R 2 =0.919-0.974, RMSE=0.235-0.442 mm day −1 ). Traore and Guven (2013) noted, in their discussion of RH and u 2, that, in an arid environment, GEP model R 2 increased significantly. RH and n are more important to ET 0 estimation in southwest China due, perhaps, to its humidity in that total n is less than in more arid regions.…”

Section: Gep Models Performance Under Different Input Combinationsmentioning

confidence: 94%

Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

Wang

Chen

et al. 2015

Theor Appl Climatol

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Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET 0 by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008)(2009)(2010)(2011)(2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET 0 calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination (R 2 ) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET 0 . In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET 0 equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models.

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Section: Gep Models Performance Under Different Input Combinationsmentioning

confidence: 94%

Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

Wang

Chen

et al. 2015

Theor Appl Climatol

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“…Terzi (2013) compared GEP, ANFIS as an alternative approach to estimate daily pan evaporation in Turkey. Traore and Guven (2013) used GEP for modelling the ET 0 using routing weather data from tropical seasonally dry regions of West Africa in Burkina Faso. This study investigates the application of the GEP and ANN for modelling daily ET ref .…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate

Yassin

Alazba

Mattar

2016

Agricultural Water Management

146

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“…The GEP model was developed for the estimation of ET o over West Africa (Traore & Guven, 2013). The authors reported a high performance of the algebraic formulation obtained using GEP for ET o simulation over the Sub-Saharan African regions.…”

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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New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa

Cited by 49 publications

References 21 publications

Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate

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

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