With this tool, variable risk for floods and potentially for droughts can be found with identification of areas particularly prone to such disasters. This information can help with preparedness for prevention and response to flood disasters. 
The system can be used to assist local governments and the agricultural sector with seasonal drought forecasting and in implementing short and long-term mitigation measures during and in advance of droughts. 
The Risk Assessment Platform is an online tool that is designed to be used to perform risk assessment using the Spatial Multi-criteria Evaluation (SMCE) method. With the facilities to upload or import datasets and creating custom criteria by defining parameters and assigning weights, the tool can be utilized in different sectors. The web application uses a powerful computational resource in the background, the Google Earth Engine, to perform spatial overlay analysis using the custom criteria set by users. 
This system guides users in applying peer-reviewed methods and cloud computing power to produce a wide variety of high-quality land cover information products that can be updated regularly and consistently. 
We make use of an International warning standard to bridge the last mile and bring efficiency to early warning practices. 
Using Sentinel-1 SNAP GPT to download and preprocess Sentinel 1 Radar Images, and push it to the Google Cloud Bucket to import in the Google Earth Engine (GEE). The Pre-processing included applying orbit file, thermal noise removel, border noise removal, calibration, multilook correction, radiometric terrain flattening, and DEM assisted co-registration, speckle filtering, and Range-Doppler Terrain Correction. Read more here. 
We investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. Read more here. 
This service provides near real-time rainfall and stream height data from publicly available satellite measurements by creation of a virtual network of rain gauges and stream gauges at points widely distributed over the entire Lower Mekong Region. 