An IoT Assisted Weather Prediction and Information Monitoring Scheme based on Intensive Learning Strategy

Several of the environmental characteristics we hold dear, including our fundamental ecology, regional climatic variances, and global variety, are threatened caused by widespread industry and increasing urbanization. Problems arise in our everyday lives as a consequence of the challenges we face due of the rapid pace at which things are changing. These problems have their origins in the rapid pace of digitalization and the lack of enough infrastructure for handling and analyzing extremely large data sets. When data generated by the IoT detection layer is inaccurate, partial, or irrelevant, it affects weather forecast reports, which in turn disrupts activities that rely on precise and reliable weather predictions. Some of the main problems in the modern world is getting accurate readings of the surroundings in real time, as doing so presents a number of obstacles. The proposed technology could avoid such events from occurring by constantly monitoring the weather. The suggested solution is built on the cutting edge of embedded system technology and the IoT. Electronic components, sensor nodes, and wireless infrastructure are also part of the system. The primary function of the sensors in this framework is to monitor environmental factors such as temperature, humidity, and gas concentrations. Data collected in this way can be transmitted to external systems, such as databases. Random Forest, Logistic Regression, AdaBoost, and Naive Bayes were all used to compare their respective identification of anomalies scores. These algorithms were applied to the time-based, temperature-based, pressure-based, and humidity-based data produced by the sensors.