A Smart Framework for Managing Natural Disasters Based on the IoT and ML

Abstract

Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review

“…Since sensors have limited resources, another way to improve the efficiency of data analysis is to combine fog computing and cloud computing [41,85]. To be specific, sensors send the data to the fog periodically [85]. After the fog pre-processes the data, it will be transmitted to the cloud [85].…”

“…To be specific, sensors send the data to the fog periodically [85]. After the fog pre-processes the data, it will be transmitted to the cloud [85]. Therefore, fog computing is mainly responsible for concentrating, distributing, caching, and analyzing the data, detecting abnormalities, analyzing the data on a smaller scale, sending notifications and feedback, and forwarding summarized data to the cloud periodically [41,85].…”

“…After the fog pre-processes the data, it will be transmitted to the cloud [85]. Therefore, fog computing is mainly responsible for concentrating, distributing, caching, and analyzing the data, detecting abnormalities, analyzing the data on a smaller scale, sending notifications and feedback, and forwarding summarized data to the cloud periodically [41,85]. Fog computing can reduce the latency of the service, respond to any emergency change immediately, and reduce the burden on the cloud [41,85].…”

Integration of Heterogeneous Sensor Systems for Disaster Responses in Smart Cities: Flooding as an Example

“…Second, IoT devices serve as a communication infrastructure for collecting real-time data from sensors and facilitate the development of monitoring systems. Aljohani et al [40] proposed a general framework for managing natural disasters using the IoT and machine learning. Third, simulation and prediction models were used to forecast potential disaster events and create early warning systems.…”

Smart Installation Weather Warning Decision Support

“…Aljohani et al utilized machine learning to classify floods. With advances in the field of machine learning, the classification of specific weather hazards such as floods has reached a remarkably high accuracy in determining the danger levels in each area of the city [10]. This capability allows for quick responses to flood disasters to minimize human and monetary loss.…”