ratnesh134/Algerian_Forest_Fire_Prediction

Problem Statement The primary objective we're addressing is the prediction of the Fire Weather Index (FWI) for the Bejaia and Sidi Bel-abbes regions in Algeria. FWI serves as a crucial indicator of fire danger, reflecting the potential for forest fires under varying weather conditions. Our goal is to leverage regression algorithms to develop a mathematical model capable of interpreting how different weather factors—such as temperature, humidity, wind speed, and rain—along with FWI components (FFMC, DMC, DC, ISI, BUI), influence the FWI. To realize this, we'll utilize historical data encompassing weather conditions and corresponding FWI values recorded over various days from June to September 2012. Training our regression models with this dataset will enable us to forecast future FWI values based on anticipated weather scenarios. Ultimately, our aim is to cultivate precise models that can reliably predict the Fire Weather Index. Such predictions are immensely beneficial for implementing fire management and prevention strategies within these specific regions of Algeria. ## Algerian Forest Fires Dataset Data Set Information: The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria. 122 instances for each region. The period from June 2012 to September 2012. The dataset includes 11 attribues and 1 output attribue (class) The 244 instances have been classified into fire(138 classes) and not fire (106 classes) classes. Attribute Information: 1. Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012) Weather data observations 2. Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42 3. RH : Relative Humidity in %: 21 to 90 4. Ws :Wind speed in km/h: 6 to 29 5. Rain: total day in mm: 0 to 16.8 FWI Components 6. Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5 7 …