# A Smart Framework for Managing Natural Disasters Based on the IoT and ML
###### Abstract
Intelligent flood detection systems are becoming necessary in the disaster management plans of cities [8]. Feasibility studies carried out when the authorities explore the possible solutions to floods point to incorporating technology into the legacy methods already in use. The most feasible technologies revolve around the Internet of Things approach based on wireless network sensors and machine learning [9]. WSNs have been heavily utilized recently due to the ease of use and maintenance coupled with their affordability and high data accuracy; wireless sensors' power usage is relatively low, thus they have more durability [10]. The wireless network sensors connect to a base station wirelessly, the central processing unit of a wireless sensor network. Many recent wireless sensors connect using wireless technologies, such as LoRa, ZigBee, NFC, Ultra-wideband, and Bluetooth [11]. Bluetooth, NFC, and Zigbee technologies consume less power but are preferable for short-range connections and, thus, are unfavorable for applications that cover vast areas. Notably, the prementioned wireless technologies are not cellular network technologies, including 2G, 3G, 4G, and 5G technologies. Cellular technologies can cover broad areas but are faced with the problem of high-power consumption; thus, they are unsuitable for sensitive systems of delay [12]. The WSNs and RFID connect to the cloud through fog computing. Fog computing is a system-level horizontal architecture that distributes computing, storage, resources, management, and networking services across the range from Cloud to Things [13]. Fog computing, in this case, minimizes the bandwidth needed and the back and onward contact between the cloud and the sensors, which could affect the entire system's functionality. Fog and cloud computing then connect, and thus refined data collected from the sensors reaches the cloud for operation. According to collected data in the cloud, and in case of any event, few governmental organizations send warning messages (SMS) to the phones for the public. This method of notifying is not effective and does not support real-time awareness for users [14]. The contributions of this research will be as follows:  
* First: A review study on flood early detection, the solutions presented in that, and the negatives and advantages of those solutions. * Second: presentation of a proposed approach platform base on a hybrid solution by integrating the IoT and machine learning algorithms. * Third: Building a smart algorithm to classify and predict the degree of risk in advance. We replaced the traditional prediction of the rainfall amount by RT classification of the level of threat according to many weather variables and previous situations. * Fourth: An actual study of actual data in the city of Jeddah, Saudi Arabia, during the previous years (2009 to 2013) and years (2018 to 2022) to prove the effectiveness of the proposed algorithm. * Fifth: Building an application to alert users and educate them in advance in the event of danger and alert the Civil Defense to take the necessary measures and a decision. Jeddah city in Saudi Arabia is our study area. It is situated on a coastal plain. The west border is the Red Sea, and the east is Hijaz Mountains. Usually, valley areas are more threatened by flooding than others, especially after heavy rains [15]. Given the city's heavy floods in 2011, 2012, 2016, and most recently, last November 2022, it was necessary to provide an efficient solution to reduce the effect of these cases. That can be achieved by a smart system providing early notifications about any potential floods. Figure 1 shows damages resulting from the last flood in 2022 in Jeddah. Following are the main and sub-sections of this paper. Firstly, a review study of the current solutions for managing disasters, especially floods. The research presents the proposed framework and classification algorithm in detail. After that, the research discusses the implementation of a real dataset and results according to standard metrics.