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HYDROCLIMATOLOGICAL APPROACH FOR MONTHLY STREAMFLOW PREDICTION USING GENETIC PROGRAMMING
An approach for monthly streamflow prediction is illustrated in this paper using the concept of hydroclimatological association. Rainfall-runoff relationship over a catchment is very complex, which may not be revealed very easily. This is due to the fact that streamflow is significantly influenced by catchment characteristics, land-use pattern, spatial distribution of rainfall, evapotranspiration over the catchment, water retention over the basin, etc. Keeping the other factors more or less constant over a sufficiently small temporal span (say monthly), intensity and spatial distribution of rainfall plays a major role behind the streamflow variation. Oceans happen to be the major source of moisture for the precipitation and the rainfall distribution over the continents is proved to be linked with Sea Surface Temperature (SST) and various large-scale atmospheric circulation patterns across the globe. Thus, the variation of basin-scale streamflow is expected to be influenced by these large-scale climatological factors, which is investigated in this paper for the Narmada River basin. The information of El Niño-Southern Oscillation (ENSO) from the tropical Pacific Ocean and Equatorial Indian Ocean Oscillation (EQUINOO) from the tropical Indian Ocean is investigated 1) for their possible influence behind the monthly streamflow variation of Narmada River at central India and 2) the efficacy of genetic programming (GP), which is an artificial intelligence technique, for the prediction of monthly streamflow through the concept of hydroclimatological approach. The results of the study indicate that GP-derived streamflow forecasting models that use historical average of monthly streamflow and the large-scale atmospheric circulation information, for basin-scale streamflow prediction are quite satisfactory. The coefficient of determination for monthly streamflow in case of Narmada River was found to be 0.921 for training and 0.836 for testing, which is quite promising for such a complex system.
HYDROCLIMATOLOGICAL APPROACH FOR MONTHLY STREAMFLOW PREDICTION USING GENETIC PROGRAMMING
An approach for monthly streamflow prediction is illustrated in this paper using the concept of hydroclimatological association. Rainfall-runoff relationship over a catchment is very complex, which may not be revealed very easily. This is due to the fact that streamflow is significantly influenced by catchment characteristics, land-use pattern, spatial distribution of rainfall, evapotranspiration over the catchment, water retention over the basin, etc. Keeping the other factors more or less constant over a sufficiently small temporal span (say monthly), intensity and spatial distribution of rainfall plays a major role behind the streamflow variation. Oceans happen to be the major source of moisture for the precipitation and the rainfall distribution over the continents is proved to be linked with Sea Surface Temperature (SST) and various large-scale atmospheric circulation patterns across the globe. Thus, the variation of basin-scale streamflow is expected to be influenced by these large-scale climatological factors, which is investigated in this paper for the Narmada River basin. The information of El Niño-Southern Oscillation (ENSO) from the tropical Pacific Ocean and Equatorial Indian Ocean Oscillation (EQUINOO) from the tropical Indian Ocean is investigated 1) for their possible influence behind the monthly streamflow variation of Narmada River at central India and 2) the efficacy of genetic programming (GP), which is an artificial intelligence technique, for the prediction of monthly streamflow through the concept of hydroclimatological approach. The results of the study indicate that GP-derived streamflow forecasting models that use historical average of monthly streamflow and the large-scale atmospheric circulation information, for basin-scale streamflow prediction are quite satisfactory. The coefficient of determination for monthly streamflow in case of Narmada River was found to be 0.921 for training and 0.836 for testing, which is quite promising for such a complex system.
HYDROCLIMATOLOGICAL APPROACH FOR MONTHLY STREAMFLOW PREDICTION USING GENETIC PROGRAMMING
Maity, Rajib (author) / Kashid, S. S. (author)
ISH Journal of Hydraulic Engineering ; 15 ; 89-107
2009-01-01
19 pages
Article (Journal)
Electronic Resource
Unknown
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