Prediction of Daily Pan Evaporation using Wavelet Neural Networks

Abghari, Hirad; Ahmadi, Hojjat; Besharat, Sina; Rezaverdinejad, Vahid

doi:10.1007/s11269-012-0096-z

Cited by 45 publications

(9 citation statements)

References 33 publications

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“…In recent years, a number of investigations into the implementation of machine learning (ML) models for evaporation estimation have been conducted across different regions (Abghari, Ahmadi, Besharat, & Rezaverdinejad, 2012;Baydaroǧlu & Koçak, 2014;Di et al, 2019;Fallah-Mehdipour, Bozorg Haddad, & Mariño, 2013;Fotovatikhah, Herrera, Shamshirband, Ardabili, & Piran, 2018;Lu et al, 2018;Majhi, Naidu, Mishra, & Satapathy, 2019;Moazenzadeh et al, 2018;Tabari, Marofi, & Sabziparvar, 2010). Several versions of ML models have been developed for evaporation modeling, including evolutionary computing, classical neural networks, kernel models, fuzzy logic, decision trees, deep learning, complementary wavelet-machine learning, and hybrid machine learning, among others (Danandeh Mehr et al, 2018;Fahimi, Yaseen, & El-shafie, 2016;Jing et al, 2019;Yaseen, Sulaiman, Deo, & Chau, 2019).…”

Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

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Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…Accurately estimation of evaporation is very important for regional water resources planning and reservoir controlling; allocation of water supplies for diverse sectors, for instance domestic, agriculture, industry and energy; and drought management (Abghari et al, 2012). Evaporation losses should be well-thought-out in the plan of various water resources and irrigation systems.…”

Section: Introductionmentioning

confidence: 99%

Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree

Kisi

2015

Journal of Hydrology

223

136

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“…In wavelet analysis, the signals are analyzed in both the time and the frequency domain by decomposing the original signals in different frequency bands using wavelet functions. The wavelet transform (WT) uses the scalable windowing technique for analyzing local variation in the time series [41]. WT provides useful decompositions of original time series, so that wavelet-transformed data improve the ability of a forecasting model by capturing useful information on various resolution levels [42].…”

Section: Wavelet Analysismentioning

confidence: 99%

A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

Zhang

Dai

De-shan

2014

Journal of Applied Mathematics

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Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between eachDsubtime series and original monthly streamflow time series are calculated.Dscomponents with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters,C,ε, andσ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

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Prediction of Daily Pan Evaporation using Wavelet Neural Networks

Cited by 45 publications

References 33 publications

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree

A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

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