Comparative Study - Three Artificial Intelligence Techniques for Rain Domain in Precipitation Forecast

Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Precipitation forecast is important in avoid incident of natural disaster which can cause loss in involved area. This review paper involves three techniques from artificial intelligence namely logistic regression, decisions tree, and random forest which used in making precipitation forecast. These combination techniques through VAR model in finding advantages and strength for every technique in forecast process. Data contains variables from rain domain. Adaptation of artificial intelligence techniques involved on rain domain enables the process to be easier and systematic for precipitation forecast.

Keywords: Logistic regression, decisions tree, random forest, VAR model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336032

References:

[1] D. J. Gagne, A. McGovern, and M. Xue, "Machine learning enhancement of storm scale ensemble precipitation forecasts,” in Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation - KDMS’11, 2011, p. 45.
[2] D. J. Yik, S. Moten, M. Ariffin, and S. S. Govindan, "Trends in Intensity and Frequency of Precipitation Extremes in Malaysia from 1951-2009,” 2009.
[3] S. Moten and M. Ariffin, "Impact of Tropical Cyclones in the West North Pacific and South China Sea on the Asian Monsoon Rainfall during the Pre-monsoon, Monsoon and Post-monsoon Seasons Subramaniam Moten and Munirah Ariffin,” 2011.
[4] Phaedon c. Kyriakidis, J. Kim, and norman l. Miller, "Geostatistical Mapping of Precipitation from Rain Gauge Data Using Atmospheric and Terrain Characteristics,” American Meteorology Society, pp. 1855–1877, 2001.
[5] J. Liu, J. Chen, and J. Ye, "Large-scale sparse logistic regression,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD’09, 2009, p. 547.
[6] S. Boyd, Convex Optimization. 2004, pp. 1–730.
[7] L. Shen and E. C. Tan, "Dimension reduction-based penalized logistic regression for cancer classification using microarray data.,” IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM, vol. 2, no. 2, pp. 166–75, 2005.
[8] S. Lee and B. Pradhan, "Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models,” Landslides, vol. 4, no. 1, pp. 33–41, Jul. 2006.
[9] L. Ayalew and H. Yamagishi, "The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan,” Geomorphology, vol. 65, no. 1–2, pp. 15–31, Feb. 2005.
[10] G. C. Ohlmacher and J. C. Davis, "Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA,” Engineering Geology, vol. 69, no. 3–4, pp. 331–343, Jun. 2003.
[11] W. c. Haneberg and A. onde. Gokce, "Rapid Water-Level Fluctuations in a Thin Colluvium Landslide West of Cincinnati, Ohio,” U.S.Geological Survey Bulletin, Professional Paper 2059-C, p. 16, 1994.
[12] P. Zhai, X. Zhang, H. Wan, and X. Pan, "Trends in Total Precipitation and Frequency of Daily Precipitation Extremes over China,” journal of climate, vol. 18, pp. 1096–1108, 2005.
[13] C. Perlich, J. S. Simonoff, and F. Provost, "Tree Induction vs . Logistic Regression: A Learning-Curve Analysis,” journal of machine learning research, vol. 4, pp. 211–255, 2003.
[14] F. Provost and P. Domings, "Tree Induction for Probability-based Ranking,” Kluwer Academic Publishers, vol. 18, no. 4, pp. 1–22, 2002.
[15] D. R. McCulloch, J. Lawry, M. Rico-Ramirez, and I. Cluckie, "Classification of Weather Radar Images using Linguistic Decision Trees with Conditional Labelling,” in 2007 IEEE International Fuzzy Systems Conference, 2007, vol. 3, pp. 1–6.
[16] A. S. Laliberte, E. L. Fredrickson, and A. Rango, "Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands,” Photogrammetric Engineering and Remote Sensing, vol. 73, no. February, pp. 197–207, 2007.
[17] J. T. Eronen, K. Puolamäki, L. Liu, K. Lintulaakso, J. Damuth, C. Janis, and M. Fortelius, "Precipitation and large herbivorous mammals I: estimates from present-day communities,” Evolutionary Ecology Research, vol. 12, pp. 217–233, 2010.
[18] B. L. Henderson, E. N. Bui, C. J. Moran, and D. a. P. Simon, "Australia-wide predictions of soil properties using decision trees,” Geoderma, vol. 124, no. 3–4, pp. 383–398, Feb. 2005.
[19] L. E. O. Breiman, "Random Forests,” Machine Learning, vol. 45, pp. 5–32, 2001.
[20] A. Liaw and M. Wiener, "Classification and Regression by random Forest,” vol. 2, no. December, 2002, pp. 18–22.
[21] J. K. Williams, D. A. Ahijevych, C. J. Kessinger, T. R. Saxen, M. Steiner, and S. Dettling, "A Machine Learning Approach to Finding Weather Regimes and Skillful Predictor Combinations for Short-Term Storm Forecasting,” National Center for Atmospheric Research, pp. 1–6, 2008.
[22] P. Cortez and A. Morais, "A Data Mining Approach to Predict Forest Fires using Meteorological Data,” Department of Information System, 2007.
[23] M. T. Series, "Vector Autoregressive Models for Multivariate Time Series,” in Time series analysis, 1994, pp. 383–427.
[24] J. G. De Gooijer and R. J. Hyndman, "25 Years of Time Series Forecasting,” International Journal of Forecasting, vol. 22, no. 3, pp. 443–473, 2006.