Use of Machine Learning Algorithms in Location Determination for Safe Construction

Abstract

Disasters are events that affect life activities that cause physical, economic and social losses. These events cause loss of life and property, as well as damage to structures such as schools and hospitals that will affect the continuation of education and health services. There are two types of disasters. The first is man-made disasters and the second is natural disasters. Natural disasters occur as a result of natural events. Earthquake is a natural disaster. Disaster management is a process that covers pre-disaster, disaster and post-disaster. This study focuses on pre-earthquake disaster management. Safe construction is necessary to reduce the effects of earthquakes. Soil class is very important in a safe construction. Soil classification was made according to TBDY-2018 by using machine learning techniques for a safe construction in the Mediterranean region. 12 different machine learning algorithms were used for Classification and the results were analyzed. As a result of the analysis, the accuracy values of the algorithms are respectively: Naive Bayes 87%, LDA 88%, KNN 84%, Adaboost 96%, Logit boost 95%, Ultraboost 92%, BF Tree 98%, Extra Tree 84%, Random Forest 93%, Random Tree%. 95, Rep Tree 96%, SimpleCart 98%. The most successful algorithms in classification are Simle Cart and BT tree. The least successful algorithm is the Extra Tree algorithm.