One of the most ubiquitous cause of worldwide deforestation and devastation of wildlife is fire. To control fire and reach the forest area in time is not always possible. Consequently, the level of destruction is often high. Therefore, predicting fires well in time and taking immediate action is of utmost importance. However, traditional fire prediction approaches often fail to detect fire in time. Therefore, a more reliable approach like the Internet of Things (IoT) needs to be adopted. IoT sensors can not only observe the real-time conditions of an area, but it can also predict fire when combined with Machine learning. This paper provides an insight into the use of Machine Learning models towards the occurrence of forest fires. In this context, eight Machine Learning algorithms: Boosted Decision Trees, Decision Forest Classifier, Decision Jungle Classifier, Averaged Perceptron, 2-Class Bayes Point Machine, Local Deep Support Vector Machine (SVM), Logistic Regression and Binary Neural Network model have been implemented. Results suggest that the Boosted decision tree model with the Area Under Curve (AUC) value of 0.78 is the most suitable candidate for a fire prediction model. Based on the results, we propose a novel IoT-based smart Fire prediction system that would consider both meteorological data and images for early fire prediction.

火是造成世界范围内森林砍伐和破坏的最普遍原因之一。控制火势并及时到达森林地区并非总是可行的。因此，破坏程度通常很高。因此，及时预测火灾并立即采取措施至关重要。但是，传统的火灾预测方法通常无法及时发现火灾。因此，需要采用一种更可靠的方法，例如物联网（IoT）。物联网传感器不仅可以观察区域的实时状况，而且结合机器学习功能还可以预测火灾。本文提供了有关使用机器学习模型来解决森林火灾的见解。在这种情况下，有八种机器学习算法：增强型决策树，决策森林分类器，已经实现了决策丛林分类器，平均感知器，2类贝叶斯点机器，局部深度支持向量机（SVM），逻辑回归和二进制神经网络模型。结果表明，曲线下面积（AUC）值为0.78的Boosted决策树模型最适合用于火灾预测模型。基于结果，我们提出了一种新颖的基于IoT的智能火灾预测系统，该系统将气象数据和图像同时用于早期火灾预测。