| Abstrak/Abstract |
Explanation from UGM team
The UGM team will explore the utilization of big data related to geospatial and hydro climatological information in Smart Decision Support Systems. Big data plays a crucial role in handling geospatial and hydro-climatological information. The integration of geospatial big data is essential for managing diverse datasets related to hydrology and meteorology, enabling precise decision-making. Utilizing big data technologies allows for the analysis of large volumes of geospatial data, including climate indicators and satellite imagery.
These tools facilitate the processing of geospatial data, aiding in tasks such as flood management. By integrating various geospatial datasets, big data technologies enhance the understanding of climate risks, supporting emergency planning and management. Big data geospatial flood prediction involves utilizing advanced technologies like machine learning and remote sensing to forecast floods accurately. Various models, such as Random Forest, Multilayer Perceptron Neural Network, and Support Vector Machines, have been employed to assess flood susceptibility based on meteorological, hydrological, and geospatial variables. These models help in creating flood susceptibility maps, highlighting areas at different risk levels.
Additionally, the integration of big data from meteorological, hydrological, geospatial, and crowdsource platforms into adaptive machine learning frameworks enhances flood forecasting accuracy. The results will provide integrated and essential information for Smart Decision Support Systems, aiding in real-time monitoring, forecasting, and early warning of flood disasters, contributing to more effective flood management. Hydraulic simulations and modeling will be conducted to support the accurate prioritization of disaster risk reduction areas. Case study analysis will identify key parameters and best practices in smart disaster management, including the establishment of the platform, the suitable algorithm, the process of engagement, and the implementation methods. Further analysis will identify the applications, requirements, parameters, and challenges in implementing Smart Decision Support Systems in the IKN area, focusing on disaster risk reduction. |
