Estimation of Monthly Pan Evaporation Using Artificial Neural Networks and Support Vector Machines

Eslamian, Seyed Saeid; Gohari, Alireza; Biabanaki, M.; Malekian, Reza

doi:10.3923/jas.2008.3497.3502

Cited by 42 publications

(20 citation statements)

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“…The ANN is a powerful soft computational technique which has been widely used in many areas of water resource management and environmental sciences [5][6][7][8][9][10][11][12][13][14][15]. ANN comprises parallel systems that are composed of Processing Elements (PE) or neurons, which are assembled in layers and connected through several links or weights.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Memarian¹,

Balasundram

2012

JWARP

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Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

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Section: Introductionmentioning

confidence: 99%

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Memarian¹,

Balasundram

2012

JWARP

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“…In recent years, artificial intelligence methods have become increasingly popular in hydrology and water resources management among researchers such as for E pan and reference evapotranspiration (ET 0 ) modeling (e.g., Sudheer et al 2002Sudheer et al , 2003Kumar et al 2002;Trajkovic et al 2003;Keskin et al 2004;Keskin and Terzi 2006;Eslamian et al 2008;Kisi 2009;Sabziparvar and Tabari 2010). Two of the artificial intelligence approaches that have recently gained popularity as an emerging and challenging computational technology are artificial neural networks (ANNs) and neuro-fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

Tabari

Talaee

Abghari

2012

Meteorol Atmos Phys

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Estimation of pan evaporation (E pan ) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CAN-FIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions (r = 0.97, RMSE = 0.81 mm day -1 , MAE = 0.63 mm day -1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.

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“…For such purposes, less enigmatic local-scale predictive models of E are immensely useful for developing actions plans in relation to drought-risk assessment, arid management and sustainable management of water-resource systems. Evaporative processes driven by meteorological variables and it's variation on space and time is non-linear (Eslamian et al 2008;Kişi 2006). Therefore, the estimation of E should be considered using soft computing models that address the inherent non-linearity in predictor datasets.…”

Section: Introductionmentioning

confidence: 99%

Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models

Deo

Samui

Kim

2015

Stoch Environ Res Risk Assess

120

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The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T max ), minimum temperature (T min ), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (&0.03) when the T max was used as an input, followed by the relatively lower value (&0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T max , verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.

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Estimation of Monthly Pan Evaporation Using Artificial Neural Networks and Support Vector Machines

Cited by 42 publications

References 0 publications

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models

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