Integrated Streamflow Forecasting System: A Step Towards Smart Flood Management - Ardiansah, Irfan; Agung, Alfonsus Mario; Asdak, Chay; Bafdal, Nurpilihan; Kastaman, Roni; Putri, Selly Harnesa; Suparno, Desy Nurliasari (Informatica, 2023)
This study aims to create an online monthly streamflow forecast system that will assist users in managing water resources and avoiding floods. We integrated a regression model into the system, utilizing historical rainfall and streamflow selective information from multiple 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. However, discrepancies between forecasted and measured values suggest that we still can improve the accuracy of the system by incorporating additional variables and more comprehensive data. Future enhancements may include the incorporation of precipitation intensity, duration, basin shape, and basin size, as well as additional validation using a broader array of field data. The developed monthly streamflow forecasting system is a valuable tool for analyzing and forecasting streamflow rates, providing a basis for making informed decisions in water resource management and flood disaster mitigation.