A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran

Hamidi, Omid; Poorolajal, Jalal; Sadeghifar, Majid; Abbasi, Hajar; Maryanaji, Zohreh; Faridi, Hamid Reza; Tapak, Leili

doi:10.1007/s00704-014-1141-z

Cited by 44 publications

(37 citation statements)

References 18 publications

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“…These Studies support our findings, where SVM demonstrated a robust tool for prediction, examples of these studies could be found in [35][36][37][38][39].…”

supporting

confidence: 76%

Towards an accurate Ground-Level Ozone Prediction

Al-Shammari¹

2018

IJECE

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This paper motivation is to find the most accurate technique to predict the ground level ozone at Al Jahra station, Kuwait. The data on the meteorological variables (air temperature, relative humidity, solar radiation, direction and speed of wind) and concentration of seven pollutants of environment (SO2, NO2, NO, CO2, CO, NMHC, and CH4) were applied to forecast the ozone concentration in atmosphere. In this report, three methods (PLS regression, support vector machine (SVM), and multiple least-square regression) were used to predict ground-level ozone. We used Fifteen parameters to evaluate the performance of methods. Multiple least-square regression, partial least square regression (PLS regression), and SVM using linear and radial kernels were the best performers with MAE (mean absolute error) of 9.17x 10-03, 9.72 x 10-03, 9.64 x 10-03, and 9.12 x 10-03, respectively. SVM with polynomial kernel had MAE of 5.46 x 10-02. These results show that these methods could be used to predict ground-level ozone concentrations at Al Jahra station in Kuwait.

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“…These Studies support our findings, where SVM demonstrated a robust tool for prediction, examples of these studies could be found in [35][36][37][38][39].…”

supporting

confidence: 76%

Towards an accurate Ground-Level Ozone Prediction

Al-Shammari¹

2018

IJECE

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“…To effectively capture useful information from training data, the choice of proper kernel function is particularly important. The radial basis function (RBF) is a simple, efficient, reliable and extensively adopted kernel and its role in the generalization ability of SVM method has been proven by previous studies [49,52]. Therefore, in the present study, RBF algorithm was used for the developed SVM models to approximate the nonlinear processes of the input-output space.…”

Section: Support Vector Machinementioning

confidence: 99%

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Dou

Yang

2017

Forests

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Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH), extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS), for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN) and support vector machine (SVM). All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP) and net ecosystem exchange (NEE), while the ANFIS model achieved the best for ecosystem respiration (R), indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET) estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.

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“…Sanghani et al [9] have made a review of soft computing techniques for time series forecasting and suggested that hybridizing of models by exploitation of the strength of individual models could be a new analysis space in time series. Hamidi et al [10] have made a comparative study of SVM and ANN in predicting precipitation in Iran and suggested that SVM model was a promising technique for predicting variations of precipitation. Jain et al [11] have made a study of time series models ARIMA and ETS using Akaike's Information Criteria (AIC) and Bayesian Information Criteria (BIS) to find out the best model for weather prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Soft Computing Techniques for Weather Change Predictions in Delhi

Mantri*,

Jayasingh

2019

IJRTE

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 Abstract: Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R 2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

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A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran

Cited by 44 publications

References 18 publications

Towards an accurate Ground-Level Ozone Prediction

Towards an accurate Ground-Level Ozone Prediction

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Soft Computing Techniques for Weather Change Predictions in Delhi

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