COMPARISON OF MACHINE LEARNING ALGORITHMS FOR FOREST FIRE PREDICTION (Term Paper Sample)

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR FOREST FIRE PREDICTION Project report submitted in partial fulfilment of the requirements for the award of the degree of Bachelor’s of Technology in Computer Science and Engineering Submitted by Shivansh Gupta - 19BCE0661 Aayushi Tiwari - 19BCE0666 Under the esteemed guidance of Dr. Umadevi K S School of Computer Science and Engineering For TECHNICAL ANSWERS FOR REAL WORLD PROBLEMS - CSE1901 Summer Special Semester 2021- 22 1773378191121 Abstract - In current times forest fires have become one of the most challenging enemies to biodiversity and nature as a whole. This paper presents two broad ways of applying machine learning algorithms; Classification and Regression to forest fire prediction and comparing their accuracy and mean-square-error respectively. The data set used in this paper is one collected from Montesinos Park in Portugal. It has 517 rows and has 13 attributes that consist of climatic and location data of the actual occurrences of forest fires. For the aforementioned prediction the researchers propose to split the data into an 85-15 split where 85% is allocated to the training of the model and 15% is allocated to testing of the model. Keywords - Machine Learning, Lasso, Ridge, Polynomial, Mean-Squared. Introduction Fire can make major hazards in this hectic world. All buildings and vehicles used in public transportation have fire prevention and fire protection systems due to the accelerated number in the fire incidents. Also, many of the firms conduct a mock fire drill in every occurrence of months to protect their employees from the fire. This would help them to understand what to do or what not to do when a fire situation happens. Forests are one of the main factors in balancing the ecology. It is very harmful when a fire occurs in a forest. But most of the time, the detection of forest fire happens when it spread over a wide region. Sometimes, it could not be possible to stop the fire. As a result, the damage of the environment is higher than predictable. The emission of large amount of carbon dioxide (CO2) from the forest fire damages the environment. Also, it can make an impact on the weather, and this make major issues like earthquakes, heavy rains, floods and so on. The forest is a large surface of area filled with trees, lots of dried leaves, woods and so on. These elements encourage the fire when it starts. The fire can be ignited through many reasons such as high temperature in summer seasons, smoking, or some parties which having fireworks. Once fire starts, it will remain until it distinguished completely. The damage and the cost for distinguish fire because of forest fire can be reduced when the fire detected early as possible. So, the fire detection is important in this scenario. Finding of the exact location of the fire and sending notification to the fire authorities soon after the occurrence of fire can make a positive impact. There are different types of fire detection methods used by the Government authorities such as satellite monitoring, tower monitoring, using sensors, optical cameras and so on. There are some other techniques used for fire suppression. The major one is burning the dry areas or like in Canada; they are using flying water tanks for fire suppression. In middle east countries, these elements sweep away and burnt it in a certain un fuelled place. But, in Australia, they provide fire in these areas and wait until it dies itself without make any danger to the wildlife or humans. A research study shows an automatic fire detection can be divided into three groups: aerial, ground and borne detection. The ground-based systems use several staring black and white video cameras are used in fire detection which detect the smoke and compares it with the natural smoke. The main benefit of using this system is high temporal resolution and spatial resolution. So that, the detection is easier. But these mechanisms still have some drawbacks in detecting the early stage of the fire. So that, it is highly important to introduce a system to detect the fire early as possible. The existing system for detecting fire are smoke alarms and heat alarms. The main disadvantage of the smoke sensor alarm and heat sensor alarms are that just one module is not enough to monitor all the potential fire prone places. The only way to prevent a fire is to be cautious all the time. Even if they are installed in every nook and corner, it just is not sufficient for an efficient output consistently. As the number of smoke sensor requirement increase the cost will also increase to its multiple. The proposed system can produce consistent and highly accurate alerts within seconds of accident of the fire. It reduces cost because only one software is enough to power the entire network of surveillance. Research is active on this field by data scientists and machine learning researchers. The real challenge is to minimise the error in detection of fire and sending alerts at the right time. The accurate prediction of the occurrence of forest fires is a challenging task. Prediction is not only knowing the exact time of the start of a forest fire but also the location where this fire occurs. Hence it is a combination of temporal and spatial prediction problems, making it more complex and challenging for the researchers. Machine learning algorithms are applied to forecast events in many real-life applications which are highly stochastic in nature, naming a few like stock market crisis forecasting stock market investment strategies, weather extreme events forecasting, predictive outage estimation and forecasting health-related issues. In the forest fire-related literature, researchers have access to lots of data coming from different resources. A set of features is selected according to the relative importance of the features considering their statistical significance. Machine learning algorithms can be helpful in a better selection of important features. Selecting only relevant and essential features may provide optimal performance with less computational and operational costs. Machine Learning algorithms can further be applied to decision making and decision support systems for forest fire prevention and fighting. Machine learning algorithms are already being used in various types of decision support and recommender systems. Machine learning algorithms can also help find strategies to place the resources (human resources, types of equipment, vehicles, etc.) optimally so that fire hazards to the forest could be minimised. Project Plan - Gantt Chart Detailed Literature Review 1 The paper displays machine learning regression techniques for predicting forest fire-prone areas. This research proposes three machine learning approaches, linear regression, ridge regression, and lasso regression algorithm. This paper uses two versions, all features are included in the first, and 70% of the features were included in the second. The paper uses a training set which is 70% of the data set, and the test set is 30% of the data set. The accuracy of the linear regression algorithm gives more accuracy than ridge regression and lasso regression algorithms. 2 The paper presents and evaluates a novel system, FireCast. FireCast combines artificial intelligence (AI) techniques with data collection strategies from geographic information systems (GIS). FireCast predicts which areas surrounding a burning wildfire have high-risk of near-future wildfire spread, based on historical fire data and using modest computational resources. FireCast is compared to a random prediction model and a commonly used wildfire spread model, Far- site, outperforming both with respect to total accuracy, recall, and F- score. 3 This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviours in the Pu Mat National Park and to predict future fires. 4 This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to resampling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. 5 The paper proposes a novel, cost-effective, machine-learning based approach that uses remote sensing data to predict forest fires in Indonesia. The prediction model achieves more than 0.81 area under the receiver operator characteristic (ROC) curve, performing sig- nificantly better than the baseline approach which never ex- ceeds 0.70 area under ROC curve on the same tasks. The model’s performance remained above 0.81 area under ROC curve even when evaluated with red...