Earthquake Prediction using WSN Data and Machine Learning
DOI:
https://doi.org/10.26438/ijcse/v8i3.5860Keywords:
WSN, SVM, KNN, Decision treeAbstract
Earthquake is sudden shaking of the ground surface caused by the movement of seismic waves through Earth’s rocks. Earthquakes are one of the major disasters and their unpredictability causes even more destruction in terms of human life and financial losses. The aim of the project is to predict the chances of earthquake using wireless sensor network data and machine learning and to alert people before the disaster occurs and save their lives. In the project a simpler way of detecting the occurrence of earthquake has been introduced. It is based on collecting WSN data using the API’s and Machine learning algorithms where weather information API is used to fetch live weather details. The collected live weather data and the previous details of the weather in a particular place are passed to Machine learning algorithms i.e. SVM, KNN, Random Forest, Decision tree and the algorithm which gives more accuracy is chosen and is applied on it to predict the current chances of disaster occurrence. If there is a chance of occurrence of the disaster (Earthquake) then an alert message is sent to the concerned authority to create awareness among people.
References
1] R S Prasanna kumar, M S Shazia Anjum, “Landslide detection using wireless sensor network in disaster monitoring” , International Journal of Innovative Research in Science ,volume 8, issue 4,pp. 4529,2019.
[2] V Maneesha. Ramesh, “Real-time Wireless Sensor Network for Landslide Detection”, IEEE Third International Conference on Sensor Technologies and Applications, pp. 405-409. 2009.
[3] J Reyes, A Morales-Esteban, F. Martı´nez-A´lvarez,”Neural networks to predict earthquakes in Chile. Applied Soft Computing”, 2013.
[4] KM Asim, M Awais, F Martı´nez–A´lvarez, T Iqbal,“Seismic activity prediction using computational intelligence techniques in northern Pakistan. ActaGeophysica”, pp.919,2017.
[5] DR Nayak, A Mahapatra, Mishra , “A survey on rainfall prediction using artificial neural network,.International Journal of Computer Applications”,pp.72,2013.
[6] Mr. S AnandBhosle1,Mr M.Laxmikant.Gavhane, “Forest Disaster management with Wireless Sensor Network” International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) , 2016.
[7] Joko Windarto, “Flood Early Warning System Developed at Garang River semarang using Information Technology based SMS and WEB”, International Journal of Geomatics and Geoscience, vol no.1,pp.14-26, 2010.
[8] MING Xiaodong XU, Wei LIU Baoyin, etc, “An Overview of the Progress on Multi-Risk Assessment”, Journal of Catastrophology, Vol 28(1), pp.126-132, 2013.
[9] Rahul R. Biswas, Tripti R. Biswas, “Modeling Seismic Effects on a Stormwater Network and Post-earthquake Recovery,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.55-61, 2017.
[10] Avinash Vibhute, Vaibhav Shinde, Amar Paranjape, “Structural Integrity Assessment of Genset Structure for Earthquake Loads, International Journal of Computer Sciences and Engineering,” Vol.7, Issue.6, pp.105-109, 2019.
[11] Rosy Mishra, Rajaram Meher, Pratibha Bhoi, Amisha Mohanty, “Cyclone and Earthquake Recognition and Estimation using HSV Colour Segmentation and Clustering”, IJSRNSC, Vol.7, Issue.4, pp.8-20, Aug-2019
[12] R.R Priyanka, M Mahesh,S.S Pallavi, Jayapala G, M.R Pooja, “Crop Protection by an alert Based System using Deep Learning Concept”Isroset,Vol.6 , Issue.6 , pp.47-49, Dec-2018
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