Total views : 302

Landslide Prediction with Rainfall Analysis using Support Vector Machine

Affiliations

  • Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, India
  • Faculty of Computing, Sathyabama University, Chennai - 600119, Tamil Nadu, India

Abstract


Objective: The paper aims in presenting a prediction model by using Support Vector Machine (SVM) technique which is meant to possess a strong capability to predict landslides by forecasting rainfall dataset using BigData concept. Methods: The dataset has been taken for the Cherapunjee region which receives the highest intensity of rainfall in India. The aim is to predict the landslide occurrence and classify the risk level associated with the landslide. To improve the reliability in landslide prediction, the proposed model uses pre-processing for removing null values in the dataset. After getting the pre-processed dataset, it will apply normalization, then SVM training and finally the Testing process. Thus the Support Vector Machine concept proved to exhibit a large degree of flexibility in handling tasks of varied complexities because of the non-linear boundary functions. Findings: The study concludes that SVM proved to be an efficient technique to forecast the landslides by predicting the rainfall in advance. The comparative results of SVM in regard with Artificial Neural Networks were proven. The study has been done specifically for Cherrapunjee region and can be implemented for any landslide prone area. Novelty/Improvement: Researchers worldwide are having a great pace to develop early prediction mechanisms for natural hazards. The study uses Radial Basis Function as an initial parameter for predicting the risk level classification of landslide. The novelty is in providing an initial selection of the kernel parameter in order to save the time on finding the best parameters.

Keywords

BigData, Hadoop, Rainfall Data, SVM.

Full Text:

 |  (PDF views: 386)

References


  • Renuga Devi S, Agarwal P, Venkatesh C, Arulmozhivarman P. Daily Rainfall Forecasting using Artificial Neural Networks for Early Warning of Landslides, IEEE, International Conference on Advances in Computing, Communications and Informatics. 2014 Sep. p. 2218–24.
  • Anbarasu K, Sengupta A, Gupta S, Sharma SP. Mechanism of activation of the Lanta Khola landslide in Sikkim Himalayas, Landslides. 2010 Jun; 7(2):135–47.
  • Sengupta A, Gupta S, Anbarasu K. Rainfall thresholds for the initiation of landslide at Lanta Khola in north Sikkim, India, National Hazards. 2010 Jan, 52(1):31–42.
  • Chandrasekaran SS, Elayaraja S, Renugadevi S. Damages to transport facilities by rainfall induced landslides during November Nilgiris, India Proceedings of the Second World Landslide Forum, Rome, 2011Oct.
  • National disaster management guidelines – Management of landslide and snow avalanches. A publication of the national disaster management authority, Government of India, 2009 Jun.
  • Zhao X, Ji M, Cui X. Research On Landslide Prediction Model Based On Support Vector Model, The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 2010 Jun; 3:540–4.
  • Pradhan B, Lee S. Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Sciences. 2009 Feb; 4 (1):1–15.
  • Anbarasu K, Sengupta A, Gupta S, Sharma SP. Mechanism of activation of the Lanta Khola landslide in Sikkim Himalayas, Springer-Verlag. 2010 Jun; 7 (2):135–47.
  • Mahajan S, Mazumdar H. Rainfall Prediction using Neural Net based Frequency Analysis Approach. International Journal of Computer Applications. 2013 Dec; 84(9):7–11.
  • Yao X, Dai FC. Support vector machine modelling of landslide susceptibility using a GIS: A case study, The Geological Society of London, IAEG. 2006; 1–12.
  • Revathy S, Parvaathavarthini B, Rajathi S. Futuristic Validation Method for Rough Fuzzy Clustering. Indian Journal of Science and Technology. 2015 Jan; 8(2):120–7.
  • Cherrapunjee dataset. Available from: http://www.cherrapunjee.com/weather-info/daily-weather-data-2009-2013/ Date accessed: 11/09/2015.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.