Abstract
Increases in greenhouse gases over the last century have caused abnormalities in the general circulation of the atmosphere. These abnormalities lead to changes in severity of climate phenomenon’s in different parts of the globe. This study aimed to simulate the maximum daily rainfall in Saravan using Artificial Neural Network (ANN). To do this, maximum 24-h rainfall of different months was obtained from synoptic station of Saravan and data of climate indicators from 1986 to 2010 obtained from NOAA website. The effective climate indicators were identified using stepwise method. The data were normalized in the range of 0.1 to 0.9 and the data were applied with 80 to 20 combinations for training data and simulation to neural network model. The used networks were back propagation and Radial Basis with Levenberg-Marquardt training algorithm which created by different combinations of inputs, number of hidden layers and the number of neurons. After creation of mass models; it was found that the chosen network model, Radial Basis, has a better function. This model, with 2 hidden layers of 12 neurons, 0.9578 determination coefficients and less error, presented more acceptable performance in the prediction stage. Comparing the results of chosen ANN and regression models showed that ANN model can accurately predict the daily maximum precipitation. It was found, that the monthly precipitation, maximum and minimum monthly relative humidity, tropical pattern of the South Atlantic Index with 7 months delay and nino1+2 Index with 10 months delay play the main role in daily maximum precipitation in Saravan.
Highlights
The Occurrence of heavy precipitation causes the flow of a large amount of water and sometimes devastating floods which is led to many damages
The results showed that these networks have a high accuracy (Hung et al, 2008) for rainfall forecasting in Bangkok, Thailand assisted from Artificial Neural Network (ANN) model
The distribution diagram of the actual and simulated amounts in the selected neural network models and regression model in Fig. 6 show that neural network model has a high accuracy for maximum daily rainfall simulation in Saravan
Summary
The Occurrence of heavy precipitation causes the flow of a large amount of water and sometimes devastating floods which is led to many damages. Poor vegetation along showery precipitation causes soil erosion, destruction of buildings and communication structures, loss of agricultural lands, water sources degradation, etc. Lee et al (1998) for estimation rainfall in Switzerland exploited from the ANNs model. Maeda et al (2001) exploited from the ANNs for prediction of precipitation in Kanto and Chubu areas in eastern Japan. Results of these two evaluations show that our method can adequately predict for the subsequent hour and is a practical tool for reducing snow hazards. The present model provides a systematic approach for runoff estimation and represents improvement in prediction accuracy over the other models studied . The present model provides a systematic approach for runoff estimation and represents improvement in prediction accuracy over the other models studied . Grimes et al (2003) with using from satellite
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