In various countries, research and implementation are ongoing to provide feasible solutions to mitigating effects of flood events. Malaysian researchers have successfully developed IoT-based monitoring systems combing wireless sensor networks, cameras and weather stations to monitor real time water levels, and incorporating related historical data to train Machine Learning models to create flood risk maps and issuing timely alerts to possible high impact communities when necessary. Pathmanathan Muniandy is the name of the smart flood detection system capable of integrating multiple data sources. The model is already in use by the Indian Meteorological Department (IMD) in a collaboration with the Google Flood Forecasting Initiative, having proven effective in improving the accuracy of flood forecasts in India.

In China, a team has used big data to forecast flooding in urban regions with the help of historical satellite and terrestrial hydrological data, having trained Machine Learning models with moderate accuracy. The research team led by Ouyang Chaojun, a research fellow at the Institute of Mountain Hazards and Environment from the Chinese Academy of Sciences, has proposed such an AI-based novel streamflow and flood forecasting model to solve flood prediction problems at a global scale for both gauged and ungauged catchments. Historical data sets across 2,089 catchments from the US, Canada, Central Europe, and the UK along with the data collection frequency of 24 hours and the time span between January 1, 1981 and December 31, 2009 was utilized to train the model, while also using historical data sets between January 1, 2010 and January 1, 2012 to verify the accuracy of the model’s forecasting capability. However, the model is still under testing and is yet to be implemented.

Indonesian researchers recently used three sources of data – rainfall rate, forest ratio and stream flow, with classification using Random Forest algorithm to build a model. The model was verified with the results being compared with the flood events of June 2022 and showed promising results with accuracy and F1 score in AUC-ROC (a method of comparison with past data) of over 90%. This model has been shown to predict flood events prior to 3 months of the day of the flood and has been integrated with a website as a warning system to issue timely warnings.

In Australia, the NSW State Emergency Service (SES) has been collaborating with the University of Technology Sydney (UTS) and TPG Telecom to develop an advanced network sensing technology using communication signals to gather localized weather data, including rainfall, water levels, and river flows. With the combination of real-time weather data with historical information from the Bureau of Meteorology and flood records, the technology enables the creation of 3D and 4D visualizations of landscape and infrastructure changes in NSW Spatial Digital Twin (SDT) model. Artificial intelligence is then used to make predictions of risks to infrastructure and communities, thereby allowing the SES to issue targeted alerts to affected areas. Although the technology is still under development, it has shown promising results and has the potential to revolutionize emergency response by providing real-time insights, addressing current challenges like limited sensor coverage and network outages during severe weather events.

Early flood warning systems, integrated with AI and IoT, can enable governments to prepare evacuation plans ahead of time as well as allowing organizations like The International Resue Committee (IRC) and GiveDirectly to provide cash transfers to susceptible groups, allowing them to prepare for disaster by purchasing essential supplies like food and medicine, customize required reinforcements to their homes, relocate to safer areas, or reinvest in businesses or recover quickly after their crops were destroyed.

In Bangladesh, adoption of similar AI and IoT strategies is capable of revolutionizing flood managements in Bangladesh. Such system would enhance early warning mechanisms, refine disaster response and help build long term resilience. Floods will remain a persistent challenge in Bangladesh, however integration of technology into disaster management and predictions can save lives and protect livelihoods of millions affected every year.