Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region

Kisi, Özgür; Cengiz, Taner Mustafa

doi:10.1007/s11269-013-0363-7

Cited by 39 publications

(19 citation statements)

References 41 publications

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“…In a future study, different lead-time forecasts in the water stage can be developed to assist the local authorities with preventing flooding effects prior to typhoon events. The soft computing techniques, such as the combining fuzzy optimal model with genetic programming [47,57], neural network and genetic programming [24,58], support vector machine [59][60][61] and the particle swarm optimization training algorithm for a neural network [29], can also be developed to improve the prediction of water stages along the river system during typhoon events.…”

Section: Discussionmentioning

confidence: 99%

“…GA has an advantage over many traditional heuristic methods when search spaces are high modal, discontinuous or constrained. It is the most popular form of evolutionary algorithm used in the diverse field of optimization problems [47]. The algorithm initializes with a population of solutions, known as chromosomes, and transforms itself by three genetic operators, selection, crossover and mutation, to obtain a better solution for the problem after each generation.…”

Section: Hybrid Neural Network and The Genetic Algorithmmentioning

confidence: 99%

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Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System

Liu

Chung

2014

Water

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Accurate simulations of river stages during typhoon events are critically important for flood control and are necessary for disaster prevention and water resources management in Taiwan. This study applies two artificial neural network (ANN) models, including the back propagation neural network (BPNN) and genetic algorithm neural network (GANN) techniques, to improve predictions from a one-dimensional flood routing hydrodynamic model regarding the water stages during typhoon events in the Danshuei River system in northern Taiwan. The hydrodynamic model is driven by freshwater discharges at the upstream boundary conditions and by the water levels at the downstream boundary condition. The model provides a sound physical basis for simulating water stages along the river. The simulated results of the hydrodynamic model show that the model cannot reproduce the water stages at different stations during typhoon events for the model calibration and verification phases. The BPNN and GANN models can improve the simulated water stages compared with the performance of the hydrodynamic model. The GANN model satisfactorily predicts water stages during the training and verification phases and exhibits the lowest values of mean absolute error, root-mean-square error and peak error compared with the simulated results at different stations using the hydrodynamic model and the BPNN model. Comparison of the simulated results shows that the GANN OPEN ACCESSWater 2014, 6 1643 model can be successfully applied to predict the water stages of the Danshuei River system during typhoon events.

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Section: Discussionmentioning

confidence: 99%

Section: Hybrid Neural Network and The Genetic Algorithmmentioning

confidence: 99%

Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System

Liu

Chung

2014

Water

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“…Several other researchers have obtained outstanding results by using the ANNs to model the evapotranspiration as a function of climatic data (e. g. Kim and Kim 2008;Kisi and Cengiz 2013;Rahimikhoob 2014). These networks were applied with only minimum and maximum daily air temperature to forecast ET o .…”

Section: Ann Modelsmentioning

confidence: 99%

“…There are numerous studies in estimation of ETo using various methods for different climate conditions (e.g. Kisi and Cengiz 2013;Perugu et al 2013;Bhartiya and Ghare 2014;Bogawski and Bednorz 2014). Specially, Snyder et al (2009) noted that ET o forecast was useful for planning irrigation, especially for high-frequency irrigation systems and shallow-rooted vegetation.…”

Section: Introductionmentioning

confidence: 99%

Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts

Luo

Traoré

Lyu

et al. 2015

Water Resour Manage

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Medium range daily reference evapotranspiration (ET o ) forecasts are very helpful for farmers or irrigation system operators for improving their irrigation scheduling. We tested four artificial neural networks (ANNs) for ET o forecasting using forecasted temperatures data retrieved from public weather forecasts. Daily meteorological data were collected to train and validate the ANNs against the Penman-Monteith (PM) model. And then, the temperature forecasts for 7-day ahead were entered into the validated ANNs to produce ET o forecast outputs. The forecasting performances of models were evaluated through comparisons between the ET o forecasted by ANNs and ET o calculated by PM from the observed meteorological data. The correlation coefficients between observed and forecasted temperatures for all stations were all greater than 0.91, and the accuracy of the minimum temperature forecast (error within ± 2°C) ranged from 68.34 to 91.61 %, whereas for the maximum temperature it ranged from 51.78 to 57.44 %. The accuracy of the ET o forecast (error within ± 1.5 mm day −1 ) ranged from 75.53 to 78.14 %, the average values of the mean absolute error ranged from 0.99 to 1.09 mm day −1 , the average values of the root mean square error ranged from 0.87 to 1.36 mm day −1 , and the average values of the correlation coefficient ranged from 0.70 to 0.75. Water Resour ManageThe results suggested that ANNs can be considered as a promising ET o forecasting tool. The forecasting performance can be improved by promoting temperature forecast accuracy.

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“…Evapotranspiration (ET) is a fundamental component of the water cycle and profoundly important for the energy cycle. An understanding of ET is crucial for myriad scientific and management issues, including hydrology (Buytaert et al, 2006;Senay et al, 2009), hydroinformatics (Va´zquez & Hampel, 2014), water resources management (Kisi & Cengiz, 2013), agricultural management (Yoder et al, 2005a), crop simulation models (Ababaei, 2014), climatology (Midgley et al, 2002), ecohydrology (D'Odorico et al, 2010), and even biodiversity (Fisher et al, 2011;Córdova et al, 2015). Evapotranspiration process is the combination of two separate processes commonly known as Evaporation and Transpiration.…”

Section: Introductionmentioning

confidence: 99%

Estimation of evapotranspiration for basilicata region with a pennman-monteith method

Khanjanzadeh¹,

Mondino²,

Morteza³

2017

JEBAS

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Estimation of reference evapotranspiration rate is a very significant parameter in agriculture science. It indicates the water consumption of the plant. So, it plays an important role in irrigation scheduling as well as plant growth modeling. In most cases, reference evapotranspiration has been computed by the Penman-Monteith equation (PM) as recommended by the FAO and applied over different climates over the globe. Information on ETc and crop coefficients (Kc) is useful for normal irrigation planning and management purposes, for the development of basic irrigation schedules, and for most hydrologic water balance studies. Therefore, according to the importance of ETc for agriculture planning and management purposes, this study was conducted in order to estimate the rate of evapotranspiration with a PennmanMonteith method for Basilicata region. The results of this experiment showed that, ETc calculated with Penman equation for arable land was 3.90 (mm/d) while it was reported 2.95 (mm/d) for Vineyard area and 3.07 (mm/d) for Pasture polfit. Result of study revealed that sample areas which were close to the lake Pertusillo, rates of ET on average higher than other areas.

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Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region

Cited by 39 publications

References 41 publications

Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System

Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System

Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts

Estimation of evapotranspiration for basilicata region with a pennman-monteith method

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