# A Smart Framework for Managing Natural Disasters Based on the IoT and ML
## 2 Related Work
### Flood Risk Management Systems
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**Research** & **The Objectives** & **Used Model** & **Result** & **Limitation** \\ \hline
[13] & To create a system that alerts users of possible disasters. & Arduino coupled with GSM and ZigBee & The developed system alerts users of possible disasters via messages. & The solution does not involve artificial intelligence and smart technologies. \\ \hline
[16] & To survey the role of roles of IoT and wireless Sensor Networks in disaster management. & IoT and Wireless Sensor Networks. & A comprehensive study detailing the application of IoT and WSN in disaster management is developed. & The systems discussed that utilize 4G suffer high packet loss. The systems using Lora WAN lacks robust fair data rate allocation and power control methods to address existing \\ \hline
[17] & To deploy next-generation phone-based ECS by using the LoRA technology for long-range, multi-hop communication among people in rescue teams. & A platform named LOCATE is developed for the purpose of deploying phone-based ECS. LOCATE is a self-localization algorithm implementing a trilateration technique, and a multi-hop dissemination scheme which combines probabilistic DTN and biased-confunction mechanisms. & The LQAs have a higher energy consumption rate thus lower flying time. LoRa technology applied has delays thus may affect real time data relay and processing \\ \hline
[18] & To develop a system that can collect and data while a in the air by means of aerial vehicles and long-distance transmission to offer new technical support for disaster management. & UAV Technology and mobile edge computing. & The study discovers the potential to combine UAV technology and Lora WAN communication into the implementation of MEC services for disaster management. & The UAVs have a higher energy consumption rate thus lower flying time. LoRa technology applied has delays thus may affect real time data relay and processing \\ \hline
[20] & The main objective of the paper is to provide a literature review of state-of-the-art machine learning algorithms for disaster and pandemic management & N/A & The study gets to discuss various ML algorithms, and models that can be used in different phases of disaster management to construct deployable models. & N/A \\ \hline
[22] & To review and choose a suitable model among different machine learning methods and algorithms, including artificial neural networks (ANN), support vector machine (SVM), multilayer perception (MLP), and long short-term memory (LSTM) which are used to forecast floods. & Long short-term memory networks are better than ANN, MLP, and SVM because long short-term memory models can learn long-term patterns better & The study establishes that long short-term memory networks are better than ANN, MLP, and SVM because long short-term memory models can learn long-term patterns better & Lack of big data limits experimenting further on the model. \\ \hline
[23] & To compare neural network models used in hydrology data processing in flood prediction systems. & Multilayer perceptron (MLP) model & The research establishes that when compared to the multiple regression longer, MLP MLP has better results in forecasting using hydrology data. & Lack of big data limits experimenting further on the model \\ \hline
[24] & To propose new approaches for spatial and temporal massive data analysis regarding natural disasters. & Neural network optimizers. & The resulting work provides a data collection model for disaster prediction, which could be utilized to collect climatic characteristics and topological characteristics in with larger samples. & The model is unsuitable for less data. \\ \hline
[25] & To analyze new approaches for spatial and temporal massive data analysis with regards to natural disasters. & N/A & The paper proposes new approaches for data handling related to natural disasters & N/A \\ \hline
[25] & To apply a one-way coupled hydrometological modelling approach by using the WRF preposition to drive a distributed hydrological model for flood simulation and forecasting. & Weather research and forecast model, Grid-Xinanjiang model & The study demonstrates the importance of temporal and spatial patterns in predicting the timing and magnitude of incoming floods & The uncertainties of coupled flood forecasting approach based on the GMM model and WRF forecasts remain unestablished. \\ \hline \hline \end{tabular}
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