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MBA 803 Security Analysis And Portfolio Management

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MBA 803 Security Analysis And Portfolio Management

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Course Code: MBA 803
University: Victoria University

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Country: Australia


Identify a problem (either practical or theoretical) that you believe could be solved (or better solved) with data mining.
Provide a thorough description of the problem and document its existence in scholarly or industry literature.
Explain why it is a problem and what the consequences are if it is not solved. 


Wildfires have increasingly become frequent natural occurrences over the past decade. These occurrence have been propagated by the intensity of the effects of climate change (Potera, 2009). Recent years have seen bigger, more aggressive and more sporadic wildfires in the traditional fire zones such as California, Australia and Greece (NASA, 2018).
However, there has also been abnormal patterns in the occurrence and spread of wildfires. A case in point for the abnormality in the spreading of wildfire is that of Portugal in 2018 (NASA, 2018). The Scandinavian countries of Sweden and Norway have also been exposed to abnormal patterns in 2018 (NASA, 2018).     
The wildfires not only destroy vegetation cover, the destruction is often extended to human life and property. This emphasizes on the need for prevention, containment and mitigation of wildfires (Potera, 2009). The advancements in technology and data mining provide an opportunity for better management of wildfires around the world.
Problem Statement
Data mining has been a revolutionary tool in the field of science, with widespread applications. Among these applications is in the management of climate change and it effects such as wildfires. Data mining has been applied in determining the factors that contribute to the occurrence of wildfires, and forecasting.
However, recent years have seen a surge of wildfires during the wildfire season of summers as well as an increase in the level of destruction. This points at an inefficiency in the predictive models developed for wildfires. Therefore, better models should be developed with focus given to the determining the most statistically significant predictors.
The research will aim at exploiting the gap in determining the most statistically significant predictors in wildfires forecasting. The result would be a better model that can be described as efficient for providing the prevention, mitigation and containment recommendations for wildfires globally.
Literature Review
Numerous research works have been done on the development of predictive techniques for wildfires occurrences. The works have employed data mining separately and together with technology to come up with tools for observing of trends and ultimately forecasting of wildfires.
Neural Network Analysis has emerged as an efficient tool for forecasting. (Safi & Bouroumi, 2011) Propose this technique in the analysis of the trend of wildfires. (Safi & Bouroumi, 2011) Cites the advantage of Neural Networks in representing relationships that are both linear and non-linear. This fits well with the dynamic nature of the predictor variables involved in prediction of wildfires.
  The research work in (Sakr, Elhajj, Mitri, & Wejinya, Artificial Intelligence for Forest Fires Prediction, 2010) focuses on the case study of Lebanon. An algorithm that is based on past weather condition forms the basis for this research work. This algorithm learns from the input data from past weather conditions in Lebanon to develop its predictive ability.
Hybrid approaches are applied in (Yu, Omar, Harrison, Sammathuria, & Nik, 2011) and (Shidik & Mustofa, 2014). In these research works clustering, classification and Neural Network Analysis are used. The clustering and classification techniques are first applied and their output used as the input for a Back Propagation Neural Network (BPNN). The hybrid approaches are used together with Forest Weather Index (FWI) and meteorological variables.
On the other hand, (Sakr, Elhajj, & Mitri, Efficient Forest Fire Occurance Prediction for Developing Countries Using Two Weather Parameters, 2011) presents a predictive model that can be used in developing countries. The model makes use of only two weather predictors thereby making it more cost effective.
This research will apply a hybrid technique that uses Regression Analysis, Clustering and Geospatial Analysis. This techniques will make use of nine different variables for prediction of wildfires that will include a human factor variable. This variable will account for the contribution of human presence and activities to wildfires. Unlike previous works, the research will also focus on the human aspect with aim being primarily on preventing the occurrence of wildfires rather than management and mitigation.
Data Collection And Preparation
This research will focus on wildfire data from the state of California in United States of America over a period of twenty years since 1999. The dataset will be divided into two subsets depending on time; 1999-2008 subset and 2009-2018 subset (which will be used for the actual analysis). The variables of interest from the 1999-2008 subset will be:

Prone Index (Frequency of occurrence of wildfires): Measured on the ratio scale and recorded cumulatively through the years.

The variables of interest from the 2009-2018 subset will be:

Human Factor Variable (Human Population Density in the area of wildfire occurrence): Measure on the ratio scale and recorded for the place of occurrence.
Prone Index (Frequency of occurrence of wildfires in an area): Measured on the ratio scale and recorded cumulatively through the years.
Vegetation Cover: Measured on the ordinal scale and recorded for the place of occurrence.
Vegetation Type: Measured on the nominal scale and recorded for the place of occurrence.
Slope: Measured on the ratio scale and recorded for the place of occurrence.
Soil Composition: Measured on the nominal scale and recorded for the place of occurrence.
Temperature: Measured on the interval scale and recorded as annual average.
Humidity: Measured on the ratio scale and recorded as annual average.
Wind Speed: Measured on the ratio scale and recorded as annual average.

The data will be collected from the websites of authorities and agencies dealing with climate, meteorology and wildfire management in the state of California. The separate variables will then be combined in excel to prepare the final subsets for analysis.
Data Analysis And Evaluation Of Results
This research will apply a stepwise multiple linear regression analysis for determining the significant predictor variables and generating the prediction model. The multiple regression analysis represents the relationship between the dependent variable and a number of independent variable in the form of an equation (Galit, Peter, Inbal, Patel, & Kenneth, 2018).
The dependent variable will be Fire Occurrence (frequency of occurrence in the given year).
The independent variables will be Human Factor, Prone Index, Vegetation Cover, Vegetation Type, Slope, Soil Composition, Seasons, Temperatures, Humidity and Wind Speed. 
The regression analysis will also be used in testing the hypothesis below:
H0: Fire Occurrence is not affected by the nine independent variables.
H1: Fire Occurrence is affected by the nine independent variables.
The final predictive model will then be used with the 1999-2008 subset to develop:

The Clustering algorithm: Both hierarchical and k-means clustering will be used to group the different places where the wildfires occur.
The Geospatial Analysis algorithm: This algorithm will map the model output based on likelihood of occurrence of a wildfire in different areas of California.

The model produced by this research will be crucial in giving more accurate prediction of the occurrences of wildfires. It will also provide more information that can be useful in the prevention of wildfires across the world.
Galit, S., Peter, B. C., Inbal, Y., Patel, N. R., & Kenneth, L. l. (2018). Data Mining for Business Analytics (1st ed.). New Delhi: John Wiley & Sons, Inc.
NASA. (2018, August 17). Topic.Fires. Retrieved from earth observatory: earthobservatory.nasa.gov/Topic/Fires
Potera, C. (2009). CLIMATE CHANGE: Challenges of Predicting Wildfire Activity. Environ Health Perspect, 1-3. 117(7).
Safi, Y., & Bouroumi, A. (2011). A Neural Network Approach for Predicting Forest Fires. 2011 International Conference on Multimedia Computing and Systems (pp. 1-5). Ouazazate, Morocco: ICMCS.
Sakr, G. E., Elhajj, I. H., & Mitri, G. (2011). Efficient Forest Fire Occurance Prediction for Developing Countries Using Two Weather Parameters. Engineering Applications of Artificial Intelligence , 888-894. 24(5).
Sakr, G. E., Elhajj, I. H., Mitri, G., & Wejinya, U. (2010). Artificial Intelligence for Forest Fires Prediction. International Conference on Advance Intelligence Mechatronics (pp. 1311-1316). Montreal, Canada: IEEE/ASME.
Shidik, G. F., & Mustofa, K. (2014). Predicting Size of Forest Fire Using Hybrid Model. Information and Communication Technology (pp. 316-327. 8407(1)). Bali, Indonesia: SpringerLink.
Yu, Y. P., Omar, R., Harrison, R. D., Sammathuria, M. K., & Nik, A. R. (2011). Pattern Clustering of Forest Fires Based on Meteorological Variables and its Classification Using Hybrid Data Mining Methods. Journal of Computational Biology and Bioinformatics Research, 47-52. 3(1).

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