Change Vulnerability Forecasting Using Deep Learning Algorithm for Southeast Asia

Amelia Ritahani Ismail, Nur ‘Atikah Binti Mohd Ali, Junaida Sulaiman

Abstract


Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (ND-GAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is applied to synthesize findings from Prediction index studies in South East Asia in dealing with vulnerability to climate change.


Full Text:

PDF

References


Hardwinarto, Sigit, and Marlon Aipassa. "Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan-Indonesia." Procedia Computer Science 59 (2015): 142-151.

Sulaiman, Junaida Binti, Herdianti Darwis, and Hideo Hirose. "Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes." Journal of Advanced Computational Intelligence and Intelligent Informatics 18.6 (2014): 999-1006.

Ezra, C. Alyosha. "Climate Change Vulnerability Assessment in the Agriculture Sector: Typhoon Santi Experience." Procedia-Social and Behavioral Sciences 216 (2016): 440-451.

Yuxin, Ding, and Zhu Siyi. "Malware detection based on deep learning algorithm." Neural Computing and Applications (2017): 1-12.

Wang, Huai-zhi, Gang-qiang Li, Gui-bing Wang, Jian-chun Peng, Hui Jiang, and Yi-tao Liu. "Deep learning based ensemble approach for probabilistic wind power forecasting." Applied Energy 188 (2017): 56-70.

A. Grover, A. Kapoor, and E. Horvitz, “A Deep Hybrid Model for Weather Forecasting,” Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15, 2015

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

A. G. Salman, B. Kanigoro, and Y. Heryadi, “Weather forecasting using deep learning techniques,” 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2015.

“ND-GAIN Index,” ND-GAIN Index. [Online]. Available: http://index.gain.org/. [Accessed: 06-Oct-2016].

B. Deshpande, “Time Series Forecasting: from windowing to predicting in RapidMiner,” http://www.simafore.com/blog/bid/110752/Time-Series-Forecasting-from-windowing-to-predicting-in-RapidMiner. Simafore. 5 November 2012. Web.




DOI: http://dx.doi.org/10.17977/um018v1i22018p74-78

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Knowledge Engineering and Data Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Flag Counter

Creative Commons License


This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats