Use of doc2vec and clustering techniques in environmental impact studies of small hydroelectric power plants in mato grosso

Abstract

This study analyzes the environmental impacts of Small Hydropower Plants (SHPs) in Mato Grosso by applying text mining and clustering techniques to examine Environmental Impact Assessments (EIAs) of these plants. With the expansion of SHPs as lower-impact alternatives, there is a growing demand for specific evaluations of their effects on fauna, flora, soil, and water resources. Using Doc2Vec to generate semantic vectors from the texts, the documents were grouped into three clusters reflecting distinct approaches: Cluster 1 focuses on broad environmental impacts and mitigation measures; Cluster 2 emphasizes water quality monitoring and erosion control; and Cluster 3 prioritizes rapid responses to soil and socioeconomic impacts. This analysis reveals how EIAs address the environmental challenges of SHPs, highlighting the importance of public policies and mitigation strategies tailored to each ecological context and providing insights for more effective and sustainable environmental planning in Mato Grosso.