scholarly journals Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods

2015 ◽  
Vol 2 (1) ◽  
pp. 87-101 ◽  
Author(s):  
A. Belayneh ◽  
J. Adamowski ◽  
B. Khalil
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Wavelet Transforms ◽  
Drought Forecasting ◽  
Short Term ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Awash River Basin

scholarly journals Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

2018 ◽  
Vol 29 (3) ◽  
pp. 320-325 ◽  
Author(s):  
Mirac Baris Usta ◽  
Koray Karabekiroglu ◽  
Berkan Sahin ◽  
Muazzez Aydin ◽  
Abdullah Bozkurt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
Autism Spectrum ◽  
Short Term ◽  
Learning Methods ◽  
Term Outcome ◽  
Machine Learning Methods ◽  
Short Term Outcome ◽  
Spectrum Disorders

scholarly journals Drought forecasting using new machine learning methods / Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod

2013 ◽  
Vol 18 (9) ◽  
pp. 3-12 ◽  
Author(s):  
Anteneh Belayneh ◽  
Jan Adamowski
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Standardized Precipitation Index ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Lead Times ◽  
Drought Forecasting ◽  
Awash River Basin ◽  
Ann Models ◽  
The Standardized Precipitation Index

Abstract In order to have effective agricultural production the impacts of drought must be mitigated. An important aspect of mitigating the impacts of drought is an effective method of forecasting future drought events. In this study, three methods of forecasting short-term drought for short lead times are explored in the Awash River Basin of Ethiopia. The Standardized Precipitation Index (SPI) was the drought index chosen to represent drought in the basin. The following machine learning techniques were explored in this study: artificial neural networks (ANNs), support vector regression (SVR), and coupled wavelet-ANNs, which pre-process input data using wavelet analysis (WA). The forecast results of all three methods were compared using two performance measures (RMSE and R2). The forecast results of this study indicate that the coupled wavelet neural network (WA-ANN) models were the most accurate models for forecasting SPI 3 (3-month SPI) and SPI 6 (6-month SPI) values over lead times of 1 and 3 months in the Awash River Basin in Ethiopia.

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