The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs

2009 ◽  
Vol 24 (10) ◽  
pp. 1233-1240 ◽  
Author(s):  
Adebayo Adeloye
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Supply ◽  
Relative Utility ◽  
Artificial Neural ◽  
Water Supply Reservoirs

scholarly journals Prediction of the quality of public water supply using artificial neural networks

2012 ◽  
Vol 61 (7) ◽  
pp. 446-459 ◽  
Author(s):  
Henrique Vicente ◽  
Susana Dias ◽  
Ana Fernandes ◽  
António Abelha ◽  
José Machado ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Supply ◽  
Public Water Supply ◽  
Artificial Neural

Hydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs)

2019 ◽  
Vol 10 (4) ◽  
pp. 39-57
Author(s):  
Raphael M. Wambua
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Surface Water ◽  
Water Supply ◽  
Lead Time ◽  
Drought Index ◽  
Hydrological Drought ◽  
Lead Times ◽  
Artificial Neural ◽  
Surface Water Supply

Hydrological drought in upper Tana River basin adversely affects water resources. In this study, a hydrological drought was forecasted using a Surface Water Supply Index (SWSI), a Streamflow Drought Index (SDI) and an Artificial Neural Networks (ANNs). The best SWSI involved combinations of rainfall and the index values integrated into ANNs. The best forecasts with SDI entailed composite functions of rainfall, stream flow and SDI. Different ANN models for both SWSI and SDI with lead times of 1 to 24 months were tested at hydrometric stations. Results show that the forecasting ability of all the networks decreased with the increase in lead-time. The best ANNs with specific architecture performed differently based on forecasting lead-time. SWSI drought forecasts were better than those of the SDI for all lead-times. The SWSI and SDI depicted R values of 0.752 and 0.732 for station 4AB05 for one-month lead-time. The findings are useful as an effective hydrological-drought early warning for viable mitigation and preparedness approaches to minimize the negative effects of drought.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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