Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran

2016 ◽  
Vol 122 ◽  
pp. 26-35 ◽  
Author(s):  
Samaneh Gharaei-Manesh ◽  
Ali Fathzadeh ◽  
Ruhollah Taghizadeh-Mehrjardi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Distribution ◽  
Decision Tree ◽  
Snow Depth ◽  
Arid Region ◽  
Tree Models ◽  
Artificial Neural ◽  
Semi Arid Region ◽  
Semi Arid

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.

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