ANALYSIS AND PREDICTION OF EARTHQUAKE IMPACT-A MACHINE LEARNING APPROACH
Mr.PARUCHURU. SRIKANTH, Mr.NETHI HEMANTH SIVA SAI, Mr.S. GOKULKRISHNAN · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2022
An earthquake is a natural calamity that is well- known for the devastation it causes to both natural and man-made structures such as buildings. Bungalows and residential locations to name a few. Earthquakes are measured using seismometers, that detect the vibrations due to seismic waves travelling through the earth’s crust. The damage caused by an earthquake was divided into damage grades in this study, with values ranging from one to five. A previously collected data set was utilised to forecast the damage grade of a given structure using a variety of criteria , which is associated with a Unique Identification String. A survey of available machine learning classifier methods was used to make the forecast. The machine learning algorithms used in this work were Logistic Regression, Naive Bayes Classifier, Random Forest Classifier and K-Nearest Neighbors. Based on an evaluation of a set of attributes, the most appropriate algorithm was considered. A detailed analysis was done on the predicted attribute by the given algorithm, followed by data analysis that provided details that could help mitigate the impact of an earthquake in future. Keywords: Logistic regression, Naive Bayes Classifiers, Random Forest Algorithm, K-Nearest Neighbors, XGBoost