Applying AI for predictive analysis in Environmental assessment:A

Sammanfattning

Environmental Assessment(EA) is an important tool in identifying, predicting and evaluating potential impacts of human activities. EA is also an important aspect in the decision-making of the environmental permitting process. In recent times, Machine Learning (ML), a subfield of Artificial Intelligence, has been established as a powerful tool for predictive analysis due to its ability to identify patterns and relationships within large datasets. This study conducts a systematic literature review to examine how ML techniques are applied for predictive analysis in Environmental Assessment and how ML techniques are integrated in the Environmental Permitting Process. This provides an overview of current research trends and the potential of Machine learning to support the Environmental Assessment and Environmental Permitting process. Using the PRISMA framework, relevant peer-reviewed studies were identified, screened and analysed to understand the commonly used ML methods, evaluation approaches, and existing research challenges.Results indicate that the ML techniques such as Artificial Neural Networks (ANN), Support Vector Machines(SVM), Random Forest (RF), Gradient Boosting, LSTM and Deep Learning are widely used in environmental areas such as air quality prediction, water quality prediction, soil evaluation, climate forecasting, environmental monitoring and risk assessment. The study reveals that the ML techniques can support decision-making in the environmental permitting process and also indicate the role of ML algorithms in prediction, permits risk assessment, automatic document analysis, and regulatory decision-making assistance. Furthermore, this study also points out several challenges, including limited data availability and limited integration of legal and regulatory variables into ML frameworks.