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- W2311352203 abstract "Prediction of any event using pattern recognition technique depends on the past history of the event. Past situations under similar conditions of the feature vector in feature space are used to predict the expected behaviour of the system. Based on this approach, a quantitative snowfall forecast model has been developed for a station in Jammu and Kashmir, using surface meteorological data of the past 12 winters (1991-92 to 2003-04, excluding the data of winter 1994-95, which was not available). The model predicts weather in terms of snow/no snow day and the amount of snowfall (snow height in cm) for three consecutive days in advance. The performance of the model has been tested for four winters for day-1, day-2 and day-3 forecasts. For qualitative snowfall forecast, the model performance for day-1, day-2 and day-3 forecasts turns out to be 80-90, 70-80 and 65-75%. The model estimates the expected snowfall amount at the station for day-1, day-2 and day-3 in advance, and based on the value of the estimated snowfall amount, it is categorized in the expected snowfall range based on the already established criterion. Quantitatively, the model predicts snowfall amount accurately for day-1 and the average accuracy of the model for different ranges of established categories varies from 25 to 55% for day-1 forecast. The model over-predicts the expected snowfall amount for day-2 and day-3 compared to day-1. The results of the models have been discussed here." @default.
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- W2311352203 date "2005-01-01" @default.
- W2311352203 modified "2023-09-24" @default.
- W2311352203 title "Weather prediction using nearest-neighbour model" @default.
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