Data-Driven Climate Change Impact Analysis Using Cross-Validation and Cybersecurity

Abstract

The effects of climate change are becoming more evident in the form of temperature variations, rainfall patterns, and air quality, and require stable, real-time, and uncompromising data-driven evaluation methods. In this paper, the researcher has suggested and demonstrated a unified system of using Internet of Things (IoT)-based environmental monitoring, cybersecurity, and statistical data analytics to understand the effects of climate change. The IoT sensors were used to collect real-time environmental data (temperature, humidity, rainfall, and air quality) and to securely transmit and store the data, using encryption and authentication protocols to maintain data integrity and reliability. The gathered data were analysed and pre-processed using descriptive statistics, time-series analysis, correlation, and regression to identify climate trends, interrelationships, and main impact drivers. The findings showed that there was considerable change in climate parameters, with increases in temperature and worsening air quality as the major contributors to environmental stress, though rainfall showed a moderating effect. The IoT-based data were verified against secondary meteorological data to ensure their accuracy and authenticity. The authors found that secure, data-driven IoT frameworks offer an effective and scalable approach to climate change impact analysis and may support informed environmental planning, risk assessment, and adaptive decision-making.