Open AccessAdvanced flood severity detection using ensemble learning models
Author(s) -
A. Sunitha Nandhini,
J Balakrishna,
Manikandan Ramesh,
Sushil Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1916/1/012048
Subject(s) - perceptron , flooding (psychology) , computer science , flood myth , cluster analysis , ensemble forecasting , artificial intelligence , ensemble learning , random forest , multilayer perceptron , machine learning , particle swarm optimization , classifier (uml) , data mining , artificial neural network , geography , psychology , archaeology , psychotherapist
On the life of human beings and financial development of the nation, the phenomenon of River flooding has its catastrophic effects. There are various approaches in finding out watercourse flooding but depleted understanding and restricted information regarding flooding conditions hinder the management estimates this particular phenomenon. The ensemble model approach has been used in this paper. (i.e. the combination of Multilayer layer perceptron model (MLP) + K-Means Clustering (KMC)) for flood severity prediction. Our ensemble way can support the modern and recent growth and development inside the IoT (IoT stands for Internet of things), with the help of some tools such as smart sensors, RFID and learning based on machine for the prediction of flood severity and its automatic analysis and it is expected to help human beings and can be a useful rescue from such kind of natural disasters. Analysis outcome indicate that ensemble model is more reliable to predict flood severity. The experimental output shows that by the usage of ensemble learning along with Multilayer Perceptron (MLP) model, Particle Swarm Optimization (PSO), K-Means Clustering, Long-Short Term Memory and Random Forest Classifier will produce an optimized result and also with greater accuracy.