Modeling monthly pan evaporations using fuzzy genetic approach

Kişi, Özgür; Tombul, Mustafa

doi:10.1016/j.jhydrol.2012.11.030

Cited by 42 publications

(23 citation statements)

References 43 publications

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“…When considering one input variable ‘ x ’ with ‘ A 1 ’, ‘ A 2 ’, …, ‘ A c ’ fuzzy subsets (A can be a linguistic parameter such as ‘low’, ‘high’, etc. ), there are the following k rules (Kisi and Tombul, ):…”

Section: Methodsmentioning

confidence: 99%

Evaporation modelling using different machine learning techniques

Wang

Kisi

et al. 2017

Intl Journal of Climatology

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Accurate prediction of pan evaporation (Ep) is critical for water resource management. This article investigates the capabilities of three different soft computing methods at estimating monthly Ep at six stations in the Yangtze River Basin using climatic factors, including the air temperature (Ta), solar radiation (Rg), air pressure (Pa) and wind speed (Ws) for the period of 1961–2000. The first part of the study focused on testing and comparing model accuracy levels at each station using local input combinations. The results indicate that the fuzzy genetic (FG) model generally produces better results than adaptive neuro‐fuzzy inference systems with grid partition (ANFIS‐GP) and M5 model tree (M5Tree) specifications in terms of the root mean square error , mean absolute error and coefficient of determination values. The performance of the above models was also examined using cross‐station applications (estimating Ep without local input or output data) in the second part of the study. The third part focused on estimating Ep using generalized FG, ANFIS‐GP and M5Tree models. Collectively, the results demonstrate that the FG model can be successfully used to estimate Ep without any local inputs and outputs and that a single generalized FG model can also be used at six different locations.

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Section: Methodsmentioning

confidence: 99%

Evaporation modelling using different machine learning techniques

Wang

Kisi

et al. 2017

Intl Journal of Climatology

View full text Add to dashboard Cite

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“…Getting the advantage of using the evolutionary mechanism, they are capable of searching large solution spaces efficiently. GA is composed of three main stages, namely, population initialization, GA operators (reproduction, crossover, and mutation) and evaluation (Kisi and Tombul, 2013). GA is a search technique drawing a growing attention in the Artificial Intelligence field.…”

Section: Fuzzy-genetic Algorithmmentioning

confidence: 99%

“…These models have regional validity because they are applicable to stations for which data were used in model development. Future researches need in generalized models which can be achieved by incorporating data from several distinct locations in model development (Kisi and Tombul, 2013). For this reason, generalized models were obtained by using the pooled data of eight stations in different climatic environments.…”

Section: Generalized Modelsmentioning

confidence: 99%

Pan evaporation modeling using six different heuristic computing methods in different climates of China

Wang

Kisi

Zounemat‐Kermani

et al. 2017

Journal of Hydrology

106

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“…One can find numerous models and methods in the literature for estimating evapotranspiration and evaporation [5,6,11]. However, in recent decades, several computational techniques and data-based statistical techniques have been successfully applied in the field of evaporation modeling using models such as ANN, Fuzzy, Neuro-fuzzy, SVM, and hybrid models [14,18,25,28]. Tan et al [26] and Piri et al [21] used ANN models for modeling daily pan/open water evaporation in dry countries.…”

Section: Introductionmentioning

confidence: 99%

“…Kisi and Cimen [15] used SVM for evapotranspiration modeling, utilizing several meteorological data from three stations (Windsor, Oakville, and Santa Rosa) in central California, USA. Recently, Kişi and Tombul [18] have used a fuzzy genetic (FG) approach for modeling monthly pan evaporation. Abdullah et al [1] used a hybrid of Artificial Neural Network-Genetic Algorithm for (ET 0 ) estimation in Iraq.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Evapotranspiration Modeling

Remesan

Mathew

2014

Hydrological Data Driven Modelling

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This chapter focuses on data-based modeling of evapotranspiration and evaporation from three totally different eco-climatic regions. In the first few sections, data-based modeling (artificial neural network) results are compared with reference to evapotranspiration (ET 0 ), estimated using traditional models from meteorological data. The second section is fully dedicated to evaporation modeling with data-based modeling concepts and input section procedures applied to evaporation modeling. In Sect. 7.1, we describe the mathematical details of the reference evapotranspiration models used. Analyses with traditional reference evapotranspiration models are performed on data from the Brue catchment, UK and the Santa Monica Station, USA. In Sect. 7.2, studies are described which have been conducted to see how data selection approaches respond to the evaporation data from the Chahnimeh reservoirs region in Iran. In this case study, we consider comprehensive use of data selection approaches and machine learning AI approaches. We have employed different model selection approaches such as GT, AIC, BIC, entropy theory (ET), and traditional approaches such as data splitting and cross correlation method on this daily evaporation data. Modeling with conventional models and hybrid wavelet based models was performed as per recommendations.

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Modeling monthly pan evaporations using fuzzy genetic approach

Cited by 42 publications

References 43 publications

Evaporation modelling using different machine learning techniques

Evaporation modelling using different machine learning techniques

Pan evaporation modeling using six different heuristic computing methods in different climates of China

Data-Based Evapotranspiration Modeling

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