Monthly evaporation forecasting using artificial neural networks and support vector machines

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Art...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Theoretical and applied climatology Ročník 124; číslo 1-2; s. 69 - 80
Hlavní autoři: Tezel, Gulay, Buyukyildiz, Meral
Médium: Journal Article
Jazyk:angličtina
Vydáno: Vienna Springer Vienna 01.04.2016
Springer Nature B.V
Témata:
Algorithms
Aquatic Pollution
Artificial intelligence
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Climate science
Climatology
Earth and Environmental Science
Earth Sciences
Evaporation
Hydrologic cycle
Hydrologic data
Hydrologic modeling
learning
meteorology
momentum
Neural networks
Original Paper
Pan evaporation
regression analysis
Relative humidity
semiarid zones
support vector machines
temperature
Waste Water Technology
Water Management
Water Pollution Control
Wind speed
Artificial Neural Network Model
Little Square Support Vector Machine
Radial Basis Function Network
Support Vector Machine
Root Mean Square Error
ISSN:0177-798X, 1434-4483
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ε-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg–Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ε-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R ²). According to the performance criteria, the ANN algorithms and ε-SVR had similar results. The ANNs and ε-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R ² = 0.905.
Bibliografie:http://dx.doi.org/10.1007/s00704-015-1392-3
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-015-1392-3