Comparison study between selected techniques of (ML, SVM and Deep Learning) regarding prediction of Flooding in Eastof Iraq

Abstract

East of Iraq Regions such as wasit city is one of the flood affected Regions, cussed by The torrents coming from Iran and the
rain water that causes great danger to the people who live near the Iraqi-Iranian border, A good amount of work carried out
by machine learning (ML) techniques and deep learning in the past for flood occurrence based on rainfall, humidity,
temperature, water flow, water level etc. The problem is that no one has attempted to predict the likelihood of a flood based
on temperature and rainfall intensity.
Therefore, we use deep learning models such as Convolutional neural network(CNN), Recurrent Neural Network(RNN),
Multi-layer Perceptron (MLP) and machine learning model such as Support vector machine(SVM), k-Nearest
Neighbors(KNN), Decision Tree(DT),Random forest(RF) and Logistic regression(LR) for predicting the occurrence of flood
based on temperature and rainfall intensity and the results were compared between the deep learning models and machine
learning models them in terms of accuracy, recall, precision and F1 Score.
The results indicate that the CNN algorithm of deep learning and KNN algorithm of machine learning and can be efficiently
used for flood forecasting with highest accuracy based on rainfall parameters, Amount of running river water and temperature
before flood occurrence.