Abstract
AbstractA novel wavelet-artificial neural network hybrid model (WA-ANN) for short-term daily inflow forecasting is proposed, using for the first time Tropical Rainfall Measuring Mission (TRMM) data...
Highlights
The economic development of any region is directly linked to the quantity and quality of its water resources
0.148 0.086 0.007 0.015 0.007 0.013 0.022 0.015 0.021 0.009 0.010 0.016 0.040 0.014 0.019 0.023 0.007 0.006 0.010 0.016 0.040 0.007 the results showed that the tan-sigmoid was the most pertinent transfer function for inflow forecasting
The approximations at level three (A3) were used as the artificial neural networks (ANN) input to perform the forecasts for seven days ahead, which results are plotted in Figs. 6(a and b)
Summary
The economic development of any region is directly linked to the quantity and quality of its water resources. Proper management of these resources is able to minimize the effects of various natural phenomena, such as droughts that directly affect the energy sector, for example. Inflow forecasting is an important issue for operation of flood and mitigation systems, operation and planning of reservoirs, hydropower generation, and many other applications, and has been discussed and published in several studies (e.g., Bertone et al 2015; Bennett et al 2016). Inflow forecasting is an important tool for the reservoir operation within such system. More efficient and robust ways to plan and operate the system are required (Hidalgo et al 2012)
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