U S I N G D ATA M I N I N G T O P R E D I C T FOREST FIRES IN AN ARIZONA FOREST

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1 Sanjeev Pandey CSC 600, Data Mining 5/15/2013 Table of Contents 1. Introduction 2. Study Area and Data Set 3. Data Mining Approaches 4. Results 5. Conclusions U S I N G D ATA M I N I N G T O P R E D I C T FOREST FIRES IN AN ARIZONA FOREST 1. Introduction Almost since the beginning of the period of westward expansion in the history of the United States, there has been an interest in predicting and preventing forest fires. These fires have had a major role in western forests for thousands of years, having many beneficial results that are important for forest ecology. However several factors have led to the rise of catastrophic, large-scale fires that until recently had been very rare. The causes of these changes are debatable, however the desire to prevent such fires from occurring is universal in cities and towns near fire-prone forests. A key component of preventing large-scale fires is early intervention. Primarily this is done through surveillance for early detection and having large numbers of fire crews and equipment available over a wide geographic area during the fire season for intervention. Given existing financial constraints on government funded land management agencies and the increasing intensities of fire seasons due to climate change and prolonged drought, there is increasing focus on improving the ability to predict when, where, and how extensive fire activity will be on a given day. There are already several methods in place for predicting fire behavior before a fire has started. One tool is the Forest Fire Weather Index, which is a Canadian empirical model developed in the 1970 s to estimate fire risk for a given day using lookup tables based upon meteorological information such as temperature, relative humidity, wind speed, and precipitation. There are also tools that use statistical models to correlate fire risk with historical weather and fire history data. One that is widely used for this purpose is called Fire Family Plus. This program is available for free from the Rocky Mountain Research Station of the US Forest Service and uses meteorological data and fire history to calculate fire danger rating indices, summarize data, and jointly analyze fire and weather data using classic statistical methods (Bradshaw and Tirmenstein 2010). While these methods can be effective, they do not attempt to quantitatively predict fire behavior based on inexpensive and readily available real-time weather data. Several studies have investigated the possibility of using data mining techniques in the study of forest fire prediction. Cortez and Morais (2006) used multiple data mining techniques including multiple regression, Decision Trees, Random Forest, Neural Networks, and a Support Vector Machine to predict burned area for forest fires in a Portuguese natural park using meteorological data from weather stations located throughout the park. They found that using a SVM model with four inputs including temperature, rain, relative humidity and wind speed, they were able to predict small fires. Another study by Stojanova et Pandey 1/8

2 al. (2011) found that using weather modeling information in conjunction with vegetation modeling and GIS data they were able to predict fire occurrence on a given day in three different regions of Slovenia. This project will apply Naive Bayes, J48 Decision Tree, and SVM algorithms using meteorological data and fire history data in two phases to build a classifier that in the first phase predicts the likelihood of a fire occurring on a day based on the weather conditions. If it is concluded that a day has a high likelihood of having at least one fire, the second phase classifier will predict the number of fires ignited on that day, the total acreage of burned area that resulted from fires that ignited on that day, and the average fire size for fires that ignited on that day. The results of this analysis will be compared to the results from the other data mining and forest fire studies to assess their effectiveness. 2. Study Area and Data Set The study area is the Coconino National Forest located in northern Arizona near the city of Flagstaff. The forest is a mix of desert, several different types of forests, riparian areas, and alpine tundra ranging in elevation from 2,600 ft to 12,633 ft. Fires have burned in the forest regularly since the time of European settlement in the 1850 s, with the months between May and July usually being the peak of the fire season. Weather data was sourced from the Western Regional Climate Center (WRCC) Remote Automated Weather Station (RAWS) Network, which makes standardized historical data from its national network of meteorological stations available on the Internet. Data from four different weather stations (Flagstaff, Greenbase, Mormon Lake, Sedona) located within the Coconino National Forest was initially used. Data ranged from 4/1/1968 to 12/31/2012 and included average and high/low daily measurements for temperature and relative humidity, average wind speed and direction, precipitation duration, precipitation amount, solar radiation, and a binary value for if the station was wet. There were a total of 14,800 data points, which is less than the expected number of days for the date range (16,345), so it is assumed that there are missing dates in the dataset. The WRCC also maintains a database of fire history for a number of US land management agencies in all states. For this project the data for the Coconino National Forest, was used which had complete fire history data from 1970 to Fire history data included a unique identifier number for each fire, ignition date, discovery date, containment date, acres burned, cause, name, subunit of the forest effected, and location (in latitude and longitude) in addition to an number of fields that were blank or mostly incomplete. In total, there were 19,193 recorded fires between 1970 and The raw data for both sets was in a non-standard text format so the program Fire Family Plus was used to import the data into a Microsoft Access database that could then be modified and exported to a csv file for use with WEKA. Preprocessing of the data utilized MS Access queries to eliminate some of the data fields from each dataset and merge the two into a single dataset using date of measurement from the weather data and ignition date for the fire history data as the key. The data was then imported into a Microsoft Excel spreadsheet for further preprocessing. A PivotTable was used to determine the total number of fires and total area burned for fires that started on the same day since there were many days during the peak of the fire season where multiple fires started on the same day. Once this was done, a field was added that calculated the average fire size for days with fire occurrence by dividing the total area burned by the total number of fires started on that day. A binary field was also added that indicated if a day had any fires at all. The data was then divided into two sets for the two phases. The first phase data set had Pandey 2/8

3 all of the weather data and the binary field for fire yes/no. The second phase data set had only weather data and fire characteristics (number, total size in acres, average size in acres) for days with fire. As recommended by Cortez and Morais (2006), a ( ln ( n + 1) ) transformation on the number of fires, acres burned, and average fire size was performed since the data tended towards days with fewer smaller fires. While preprocessing it was found that the Sedona, Greenbase, and Mormon Lake stations had incomplete data. It was also found that even though the fire history data had a field identifying the location within the forest where the fire occurred, it was difficult to properly correlate the closest weather stations to a specific fire. For these reasons, the dataset was filtered to use only data from the Flagstaff weather station. Flagstaff is located in the middle of the Coconino National Forest in terms of location and elevation, so its data is mostly the average for the entire forest as a whole. Once the dataset was loaded in WEKA, the weather data for both data sets was discretized using default settings (10 bins, not equal frequency). The fire characteristics for phase 1 were discretized into two bins (fire yes, fire no). The fire characteristics for phase 2 were discretized into 10 bins with equal frequency since most of the data was for low numbers and low acreages of fires for a day. Fig. 1: Raw weather data, phase 1 Pandey 3/8

4 Fig 2: Discretized weather data, phase 1 Fig 3: Discretized data for phase 1, predicting fire occurrence (blue is no fire, red is fire) Pandey 4/8

5 Fig 4: Discretized data for phase 2, fire attributes only. Bottom row is transformed data (ln (n+1)) 3. Data Mining Approaches! The data mining algorithms used were all implemented from within the WEKA machine learning software suite. The classifier methods used include Naive Bayes, J48 decision trees, Logistic Regression, and Support Vector Machines. Classifiers were run with default settings in all cases and using 10-fold cross validation.! For Phase 1, Naive Bayes and J48 were run along with the Simple Logistic algorithm to provide comparison to the results of a statistical model such as the one used in the Fire Family Plus software. For Phase 2, Naive Bayes, J48, and SVM (using the built-in WEKA classify function SMOReg) were run. 4. Results The first phase classifiers had the following results: Accuracy Precision Recall J % Naive Bayes 75.7% Simple Logistic 76.2% Table 1: Phase 1 classifier results Since phase 1 of the project bore a strong resemblance to the Stojanova et al. (2011) Slovenian study, comparison was made to their results. In that study, the researchers stated that since the purpose of the study was to estimate the risk of fire outbreak with a probability value for the occurrence of fire given the Pandey 5/8

6 values of the input data, accuracy of the classifier should be considered in conjunction with other quality measures such as precision, recall, Kappa, and Area Under the ROC Curve. Higher precision indicates fewer false positive results, which is important in the case of predicting fire outbreaks since more false positives can lead to a waste of resources. Higher recall indicates that positive results are correctly predicted. This is also very important in predicting fire outbreaks since it means that fewer fires occur on days that are predicted as non-fire days. In my results, all of the classifiers performed with nearly equal accuracy. The J48 was slightly better than the other two models and the Naive Bayes performed slightly worse than the other two models. For the precision indicator, all three models performed virtually the same, but in terms of recall the J48 and Simple Logistic models performed better than the Naive Bayes. The accuracy, precision and recall values for this project s models were comparable to the results from the Slovenian study using the same classifier methods. The Slovenian study had 4 separate regions that were modeled and the results from my study generally fell within the range of values that they found. Their results were better in the best cases with some classifiers having 86% accuracy (Bagging J48), but also worse in some cases where they had accuracy as low as 63% (Naive Bayes). It must be noted that the input data used in this study was very different from the data used to construct the models in the Slovenian study since they used predicted weather data along with vegetation models and geographic data while this study only used historical weather data. Phase 2 of the project is comparable to the Cortez and Morais study which attempted to predict fire size using historical weather data. In that study, several Data Mining methods were employed including Random Forest, Decision Tree, Neural Network, Support Vector Machine, and Multiple Regression. Overall performance was measured by comparing the mean absolute deviation and root mean squared values for the classifiers. Accuracy was also included for comparison to results of phase 1. The results of this project did not seem to compare favorably to the results found in Cortez and Morais. N Size Average Size J48 43% (0.149) (0.273) 25% (0.169) (0.311) 44% (0.153) (0.277) NB 36% (0.137) (0.278) 23% (0.165) (0.299) 38% (0.152) (0.284) SVM N/A 29% (0.306) (0.404) N/A Table 2: Accuracy (Mean Absolute Error) (RMSE) of classifiers for 3 different attributes 5. Conclusion! In this project, a data mining approach was employed to predict occurrence of fire using daily weather data and the characteristics of fire occurrences on days with fire using that same weather data. The models were able to predict fire occurrence with 76% accuracy, which is approximately equal to the predictive accuracy of statistical models using the same data. The models were less accurate in predicting characteristics of fire on days where fires occurred, including area burned, number of fires, and average fire size. Pandey 6/8

7 ! The findings for predicting fire occurrence were in line with other studies. This is probably because fire is mostly limited to only part of the year with winters having little to no fire activity and summers having the bulk of the fire activity. Since many of the weather indicators are functions of season, it makes sense that they could in turn predict fire occurrence with some accuracy.! The findings for predicting characteristics of fire, on the other hand, were inconclusive. There are several possible causes for the difficulty in modeling the fire characteristics. One reason could be that the total size burned may not necessarily relate to the size burned on the day of ignition. For example, a fire that burns for several days may be small for a number of days and then blow up in a single day. A better number to use for area burned might be a day-to-day total for a fire that burns over several days. However any value that is used for area burned has many other factors involved such as time to detection, resources allocated to fight the fire, and number of ignition points. Also, there may not be extensive data on day-today area burned for multi-day fires since this was difficult to track until fairly recently.! Performance of the model may also be strongly effected by the binning strategy used when discretizing the data. Initially I used 10 bins with unequal frequency for the attributes that the model attempts to predict, but the resulting classifiers seemed overly optimistic probably since a majority of the cases were in the first bin. I changed the discretizing function to use the same number of bins with equal frequency and the classifiers performed much worse. It may work better to reduce the number of bins, but that may also reduce the meaningfulness of the resulting model since the prediction will be of a lower resolution. The natural log transformation of the attributes had no discernible effect on the predictive accuracy of the models, probably since the data was discretized after the transformation which ended up making the untransformed data and transformed data virtually identical.! The models for phase 2 of the project were limited to only 5,754 input points. since it included only fire days Increasing the number of fire occurrences might be useful in training the classifier. To do this, one could include fire history information from other land management blocks with similar vegetation and geography. Good candidates could be the neighboring Kaibab, Prescott, and Apache-Sitgreaves National Forests along with other National Parks and Monuments, and Bureau of Land Management parcels in Arizona, New Mexico and the greater Southwest.! In comparing the results for prediction of fire characteristics with those of the study in Portugal, it could be that the models for the Coconino National Forest were simply overgeneralizing the diversity of the landscape. The Portuguese study was for an area that was 1/10th the size of the Coconino NF but with a higher density of weather stations. In this way, the classifier algorithms may be benefited by either limiting classification to the area in the direct proximity of a weather station or increase the number of weather stations used and correlate fires directly to a weather station near their ignition point. Fires that occurred outside the area of any possibly representative weather stations would not be used. However, using such an approach may lead to a model that is over-fitted to a specific region that would therefore have little use to land managers outside of that small area.! Overall a thorough comparison to Coretz and Morais could not be made since the analysis they used was much more sophisticated than the analysis for this project. In the end I was unable to faithfully reproduce their methods of data preprocessing and classifier development due to the use of different software to perform the analysis (WEKA vs. RMiner library for the R statistical environment) and a basic lack of understanding of their approach. I attempted to recreate their results since their dataset was publicly Pandey 7/8

8 available, but I am not sure I interpreted the output from WEKA correctly since the results did not compare correctly to the published results. The authors described the data set as a difficult regression task, perhaps as a warning. 6. Works Cited Bradshaw, L.S. and Tirmenstein, D. (2010). FireFamilyPlus Version 4 User s Guide. Draft Edition. Boise, ID. Cortez, Paulo and A Morais. (2006). A Data Mining Approach to Predict Forest Fires using Meteorological! Data. Department of Information Systems/R&D Algoritmi Centre, University of Minho. D. Stojanova, P. Panov, A. Kobler, S. Dzeroski, and K. Taskova. (2011) Estimating the risk of fire outbreaks in the natural environment.. Data Min Knowl Disc, March 2012, Volume 24, Issue 2, pp Witten I, Frank E (2005) Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. Morgan! Kaufmann, San Francisco. Pandey 8/8

Will it rain tomorrow?

Will it rain tomorrow? Bilal Ahmed - 561539 Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia bahmad@student.unimelb.edu.au Abstract With the availability