An Integrated Data Science Pipeline for Biological and Environmental Insights: AI-Driven Trend Detection Aligned with Ecological and Sustainable Development Goals

Abstract

The main objective of this work is to develop a comprehensive data science pipeline for the collection, integration, and analysis of biological, ecological, and environmental datasets to support ecological goals and the Sustainable Development Agenda. The system aims to enable data-driven insights that contribute to sustainable growth and environmental preservation. The methodology involves gathering data from governmental websites using web scraping techniques and custom-developed tools, along with accepting experimental or observational data from independent sources. A variety of AI techniques, such as cloud-based platforms and locally optimized models tailored for ecological research and SDGs, are implemented for analytical processing. The results indicate that the proposed pipeline facilitates improved pattern recognition, comparative analysis, and predictive evaluation for ecological and environmental sustainability. Its modular and adaptive framework supports extensions such as automated reporting and real-time environmental monitoring.