IoT-BASED CLIMATE CHANGE PREDICTION SYSTEM

Abstract

Climate change is one of the most significant challenges to every country's development, ravaging havoc on the lives of all people on this planet. Researchers have raised numerous research and studies of strategies for tracking climate change. The current climate change tracking method in Rwanda employs a weather station model, in which numerous fixed weather stations are installed throughout the country; however, due to its immobility, this process cannot cover the entire country. With the lack of advanced methodologies and technology, the process of climate change tracking has become extremely expensive and suffered inaccuracies due to a lack of proper knowledge of analyzing collected data, and the lack of specific accurate hardware. Throughout this research, with the use of the MQ-135 and DHT11 sensors, ESP8266 collects carbon dioxide gas and temperature/humidity respectively and other component include a push button for detecting the current season. ESP8266 is programmed to send data over MQTT protocol, which uses Wi-Fi capability to send data to MQTT Broker. Using the MQTT protocol's Publish/Subscribe criteria, node-red subscribes to the topics defined in the MQTT broker to obtain data, which is then sent to MongoDB for permanent storage and also fed into the machine learning model for climate change/warming prediction. Different algorithms are used to evaluate this model. As result, Random Forest classifier approves itself to be the best model in evaluating the built model. This study shows that the increase in carbon dioxide gas leads to the gradual increase in the environmental temperature. Finally, the prediction clarifies that if no measures are taken presently, the climate change in Rwanda's Industrial zone will be dominated by warming periods in the future.