Abstract
In hydrological cycle, precipitation initiates the flow and governs the system. The preciseness in the prediction of rainfall will reduce the uncertainty involved in estimating the associated hydrological variables such as runoff, infiltration, and stream flow. Many research works has been channelled towards improving the accuracy of these predictions. ANN is the most widely used neural networks in Integrated Water Resource Management. Most of these models, utilize the strength of data-driven modelling approach. The reliability of these predictions depends on the preciseness in selecting the correlated variables. If the available historical database fails to record the most correlated variable, then reliability on these data-driven approach predictions is questionable. In this paper, an attempt has been made to develop a methodological framework that utilizes the strength of a predictive data-mining analysis (decision tree). The developed decision tree based rainfall prediction model maps the climatic variables, namely; a) temperature, b) humidity, and c) wind speed over the observed rainfall database. The performance of the developed model is evaluated based on three performance indicators (Nash Sutcliffe efficiency, RMSE and MSE). The performance of the developed model is also compared with the well- known data-driven (Artificial Neural Network) based rainfall prediction model.
Highlights
The decisions taken by the policy makers or research community on water resource management depend upon the predicted/forecasted scenarios generated by the models
For achieving the desired objective of this proposed research, a methodological framework has been developed by utilizing the strength of descriptive data mining (Decision tree) algorithm for recovering hidden relationships that exists in the rainfall and climatic variables
The prepared database has been explored using Decision tree (DT) data-mining algorithm for recovering the hidden relationship that exists between the input and output variables
Summary
The decisions taken by the policy makers or research community on water resource management depend upon the predicted/forecasted scenarios generated by the models. To ensure the dependability on the precipitation predictions, in the recent past many models had been proposed namely time series prediction and data driven models. In the case of prediction or forecast based on previous rainfall events is concerned, the most popular and well known technique of time series forecast is regression analysis and moving average. To increase the level of prediction of ARIMA model many improvements had been suggested by researchers. In these statistical techniques, the present time rainfall depends upon the pervious rainfall and statistical parameters of the historical rainfall database. The accuracy in statistical parameter is ensured by the length of historical database. If the database is of limited/sparse in number, dependability on the time series forecasted rainfall pose lot of uncertainty
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