A Real-Time Flood Detection System Based on Machine Learning Algorithms with Emphasis on Deep Learning
How to Cite?
Abdirahman Osman Hashi, Abdullahi Ahmed Abdirahman, Mohamed Abdirahman Elmi, Siti Zaiton Mohd Hashi, Octavio Ernesto Romo Rodriguez, "A Real-Time Flood Detection System Based on Machine Learning Algorithms with Emphasis on Deep Learning," International Journal of Engineering Trends and Technology, vol. 69, no. 5, pp. 249-256, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I5P232
A flood is expressed as water overflowing onto the ground, that usually is dry, or an increase of water that has a significant impact on human life, and it is also declared as one of the most usual natural phenomena, causing severe financial damage to goods and properties, as well as affecting human lives. However, preventing such floods would be useful to the inhabitants in order to get sufficient time to evacuate in the areas that might be susceptible to floods before they happen. Regarding the issue of floods, numerous scholars proposed different solutions, for instance, developing prediction models and building a proper infrastructure. Nevertheless, from an economical perspective, these proposed solutions are inefficient for people in countries like Somalia, for instance. Hence, the main objective of the present research paper is to propose a novel and robust model, which is a real-time flood detection system based on Machine-Learning-algorithms and Deep Learning; Random Forest, Naive Bayes J48, and Convolutional Neural Networks that can detect water level and measure floods with possible humanitarian consequences before they occur. The experimental results of this proposed method will be the solution to forth mentioned problems and conduct research on how it can be easily simulating a novel way that detects water levels using a hybrid model based on Arduino with GSM modems. Based on the analysis, the Random-Forest algorithm outperformed other machine learning models regarding the accuracy compared to the alternative classification methods with 98.7% of accuracy. In contrast, 88.4% and 84.2% were achieved using Naive Bayes and J48, respectively. On the other hand, using a Deep Learning approach achieved 87% of accuracy, showing overall good results on precision and recall. The proposed method has contributed to the field of study by introducing a new way of preventing floods in the field of Artificial Intelligence, data mining, and Deep Learning.
Machine Learning, Naive Bayes, Random Forest, Artificial Intelligence, Convolutional Neural Network, Data Mining, Natural Language Processing
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