Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems

Abstract

Floods after monsoon rains are frequent disasters that affect millions of lives in Pakistan. Human lives are lost, agriculture economies are destroyed, and livestock animals, houses, fruit farms, and crops are lost which are the major livelihoods of thousands of people in Punjab. Each year there are heavy rains in the monsoon season and, due to global warming, there is the rapid melting of snow in northern glaciers; these factors subsequently cause floods. There is also loss of life due to the spread of waterborne diseases and snake bites. Flood monitoring provides early detection of a flood and the calculation of its intensity, which results in reduced human life losses and economic losses. Most casualties are caused by the lack of timely real-time, authentic information about the high-risk areas, and flood intensity, speed, and direction. Therefore, the proposed approach is centered on formal modeling and verification of safety and liveness properties of flood monitoring perceivers. Each flood perceiver has several sensors. It requires the collection of information starting from the flood perceiver, observer, and environmental forecast. This information is processed to determine the flood intensity level. We have developed a CP-Nets’ formal model and model-checked it. We have verified the safety and liveness properties of correctness by exhaustive verification of the system using model-based proof obligations (Event-B method using Rodin). Our objective in this research is to propose a correct, reliable, and efficient flood warning, monitoring, and rescue (WMR) SoS based on formal methods. We have used formal modeling and model-checking based on state-of-the-art hierarchical CP-Nets supported by exhaustive formal proof obligations of Event-B.

Machine learning-based Prediction of Cotton farming using ARIMA Model

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