Inflow Forecasting For Lakes Using Artificial
Neural Networks
Author(s)
R. K. Suryawanshi1, S. S. Gedam1 & R. N. Sankhua2
Abstract
Artificial lakes or reservoirs are constructed for storing the flood waters available
during the rainfall period which can be subsequently used for meeting various
water demands. These storage areas are also useful for mitigating the damage
caused by floods and droughts. Generally, considering the
geographical/topographical aspects, many of the artificial lakes are ideally
located in the upper reaches of the catchments where the rainfall is
comparatively high and the submergence is comparatively low. In such small
catchments, severe storms result in very high inflows entering into the storage
areas in a short duration. The conventional tools for inflow forecasting include
the stochastic approach in which the results depend on the quantity and quality of
the input data. To overcome such limitations, the use of present computing
capabilities, Satellite Remote Sensing (SRS) data, Geographical Information
System (GIS) as well as Artificial Neural Networks (ANN) tools for the
development of a real time inflow forecasting model based on the observed
rainfall pattern to advance the forecast time as well as to achieve better forecast
accuracy would help immensely in planning the lake operations.This study
includes the methodology developed for inflow forecasting using geospatial tools
comprising spatial and temporal analysis of the storm events i.e. digital rainfall
modeling, estimation of runoff depths using ANN and conversion of runoff
depths into inflows. However, this paper discusses a digital rainfall model for
isochrone areas of the catchment for spatial rainfall distribution and estimation of
runoff depths using ANN. An application example for the Panshet reservoir in
the Khadakwasla complex near Pune (India) is used for model calibration and
presented.
Keywords: digital rainfall, isochrones, ANN, GIS, SRS, inflow forecasting.
Keywords
digital rainfall, isochrones, ANN, GIS, SRS, inflow forecasting
Related Book
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