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Prediction of Forest Fire Risk to Trigger IoTs Reconfiguration Action

机译：森林火灾风险预测触发物有所重新配置动作

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摘要

Nowadays, the ever-increasing demand for IoT nodes for adapting to changing environment conditions and application requirements rapidly raising the need of reconfiguring already existing IoTs nodes. Forest fires is one of the main causes of environmental degradation and its detection and prediction is a challenge, a case study of predicting IoTs reconfiguration. Each prediction algorithm has its own advantages and disadvantages, which lead to different predictive results for each specific IoT application reconfiguration behavior. It is important to determine which set of metrics are effective for predicting. The objective of this work is to choose the most suitable prediction algorithms for detection of Forest Fire Risk for trigger IoTs reconfiguration actions. In this work, a comparative study between various prediction algorithms is carried out. The performance metric is based on the accuracy, precision, recall, and the training time. The experimental results show that Feedforward Neural Network is the most accurate that gives a good prediction accuracy.
机译：如今，对IOT节点的需求不断增加，以适应更改环境条件和应用要求，迅速提高重新配置现有的IOT节点的需要。森林火灾是环境退化的主要原因之一，其检测和预测是一种挑战，案例研究预测IOTS重新配置。每个预测算法具有自身的优点和缺点，这导致每个特定的物联网应用重新配置行为的不同预测结果。重要的是要确定哪一组指标对于预测是有效的。这项工作的目的是选择最合适的预测算法，用于检测触发物有所重新配置动作的森林火灾风险。在这项工作中，进行了各种预测算法之间的比较研究。性能度量基于精度，精度，召回和训练时间。实验结果表明，前馈神经网络是良好的预测准确性最准确的。

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