Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs

Adnan, Rana Muhammad; Malik, Anurag; Kumar, Anil; Parmar, Kulwinder Singh; Kişi, Özgür

doi:10.1007/s12517-019-4781-6

Cited by 62 publications

(19 citation statements)

References 34 publications

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“…Evapotranspiration (ET) as an important hydrological parameter is affected by weather variables such as temperature, relative humidity, wind speed, solar radiation, and so on. Subsequently, climate change can influence ET as confirmed by the previous investigations [23][24][25]. Harmsen et al [26] evaluated the effect of climate change on the reference ET in a study in 2009 and he found decreasing crop yields in Puerto Rico under different scenarios.…”

Section: Introductionmentioning

confidence: 74%

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Bayatvarkeshi

Zhang

Fasihi

et al. 2020

Water

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This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.

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Section: Introductionmentioning

confidence: 74%

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Bayatvarkeshi

Zhang

Fasihi

et al. 2020

Water

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“…Evaporation is generally measured using two methods: (i) direct methods such as Class A panevaporimeter and (ii) indirect methods include empirical equations (Ghorbani, Deo, Yaseen, Kashani, & Mohammadi, 2017). Doorenbos and Pruitt (1977) said that the Class A pan-evaporimeter performance was affected by support vector machine (SVM), evolutionary computing, data mining and complementary wavelet-AI models (Adnan, Malik, Kumar, Parmar, & Kisi, 2019;Guven & Kisi, 2013;Kisi & Heddam, 2019;Qasem et al, 2019;Qutbudin et al, 2019;Rezaie-Balf, Kisi, & Chua, 2019;Sebbar, Heddam, & Djemili, 2019;Yaseen, El-shafie, Jaafar, Afan, & Sayl, 2015). Tabari, Talaee, and Abghari (2012) predicted daily pan-evaporation using CANFIS and MLPNN techniques in Iran.…”

Section: Introductionmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

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“…Due to the complexity of estimating the ETo-based physical process, several machine learning methods were employed to estimate the ETo during the last two decades of the 21st century [ 8 ]. More recently, during the last two decades, artificial neural network (ANN) models have been examined for estimating the ETo, such as the multi-layer perceptron neural network (MLP-NN), fuzzy logic (FL), the adaptive neuro-fuzzy neural network (ANFIS), and least square support vector regression (LSSVR) [ 8 , 9 , 10 , 11 ]. The motivation for utilizing the ANN models is that these methods can provide high accuracy and robustness, are modeless, and can easily handle big data [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Modeling Using New Heuristic Methods

Chen

Yuan

Kisi

et al. 2020

Entropy

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The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

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Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs

Cited by 62 publications

References 34 publications

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Reference Evapotranspiration Modeling Using New Heuristic Methods

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