Earthquake Prediction using Machine Learning Algorithm
Pratiksha Bangar1, Deeksha Gupta2, Sonali Gaikwad3, Bhagyashree Marekar4, Jyoti Patil5
1Pratiksha Banagr, Department of Information Technology, Jayawantrao Sawant College Of Engineering, Pune, India.
2Deeksha Gupta, Department Of Information Technology, Jayawantrao Sawant College Of Engineering, Pune, India.
3Sonali Gaikwad, Department Of Information Technology, Jayawantrao Sawant College Of Engineering, Pune, India.
4Bhagyashree Marekar, Department Of Information Technology, Jayawantrao Sawant College Of Engineering, Pune, India.
5Jyoti Patil, Ph. D. Research Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P. India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4684-4688 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9110038620/2020©BEIESP | DOI: 10.35940/ijrte.E9110.018620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Per the statistics received from BBC, data varies for every earthquake occurred till date. Approximately, up to thousands are dead, about 50,000 are injured, around 1-3 Million are dislocated, while a significant amount go missing and homeless. Almost 100% structural damage is experienced. It also affects the economic loss, varying from 10 to 16 million dollars. A magnitude corresponding to 5 and above is classified as deadliest. The most life-threatening earthquake occurred till date took place in Indonesia where about 3 million were dead, 1-2 million were injured and the structural damage accounted to 100%. Hence, the consequences of earthquake are devastating and are not limited to loss and damage of living as well as non-living, but it also causes significant amount of change-from surrounding and lifestyle to economic. Every such parameter desiderates into forecasting earthquake. A couple of minutes’ notice and individuals can act to shield themselves from damage and demise; can decrease harm and monetary misfortunes, and property, characteristic assets can be secured. In current scenario, an accurate forecaster is designed and developed, a system that will forecast the catastrophe. It focuses on detecting early signs of earthquake by using machine learning algorithms. System is entitled to basic steps of developing learning systems along with life cycle of data science. Data-sets for Indian sub-continental along with rest of the World are collected from government sources. Pre-processing of data is followed by construction of stacking model that combines Random Forest and Support Vector Machine Algorithms. Algorithms develop this mathematical model reliant on “training data-set”. Model looks for pattern that leads to catastrophe and adapt to it in its building, so as to settle on choices and forecasts without being expressly customized to play out the task. After forecast, we broadcast the message to government officials and across various platforms. The focus of information to obtain is keenly represented by the 3 factors – Time, Locality and Magnitude.
Keywords: Earthquake, Forecast, Machine Learning, Ran-dom Forest, Support vector Machine.
Scope of the Article: Machine Learning.