Predicting catchment flow in a semi-arid region via an artificial neural network technique

2004 ◽  
Vol 18 (13) ◽  
pp. 2387-2393 ◽  
Author(s):  
S. Riad ◽  
J. Mania ◽  
L. Bouchaou ◽  
Y. Najjar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Arid Region ◽  
Artificial Neural ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

2021 ◽  
Vol 32 (4) ◽  
pp. 1-11
Author(s):  
Roohul Abad Khan ◽  
Rachida El Morabet ◽  
Javed Mallick ◽  
Mohammed Azam ◽  
Viola Vambol ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Arid Region ◽  
Prediction Models ◽  
Moving Average ◽  
Mountainous Region ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Semi Arid Region ◽  
Semi Arid

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.

scholarly journals Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Thabo Michael Bafitlhile ◽  
Zhijia Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Optimization Method ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Climatic System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Semi Arid

The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.

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