Integrated Streamflow Forecasting System: A Step Towards Smart Flood Management

Abstract

This study aims to help users manage water resources and prevent flooding by creating an online monthly streamflow forecasting system. We have integrated a regression model into the system, using historical information on rainfall and streamflow selectivity from a number of monitoring stations in the Upper Cimanuk sub-basin. Users can access the online system to input and view rainfall and streamflow data and enumerate monthly streamflow rate projections. To verify the system's forecast accuracy, we compared it with manual calculations employing the velocity-area method and field observations. The system provides reasonably accurate forecasts, as indicated by the system's high coefficient of determination (R2) value of 0.91. Nevertheless, the differences between predictions and measurements suggest there is scope to improve the accuracy of the system by including additional variables and more comprehensive data. Future enhancements may include additional validation using a wider range of field data, as well as the inclusion of precipitation intensity, duration, catchment shape and size. The developed monthly streamflow forecasting system is a valuable tool for analyzing and forecasting streamflow rates, providing a basis for informed decision making in water resource management and flood disaster mitigation.