LEVERAGING CROSS-DOMAIN MACHINE LEARNING APPROACHES FOR ENVIRONMENTAL MONITORING AND PREDICTIVE MODELING

Abstract

The prediction and environmental surveillance are crucial in the process of addressing environmental problems in the world, including climate change, pollution, and destruction of resources. Machine learning (ML) has been demonstrated as a potential improvement in measuring the precision and efficacy of such procedures. Nonetheless, conventional models of ML have certain shortcomings to be utilized in various areas of the environment as a result of data sparsity and domain-specific particularities. The current paper discusses how cross-domain machine learning practices, in this case, transfer learning, can be used to enhance predictive performance and model flexibility in the environmental monitoring systems. Through the cross-domain machine learning approach, where models trained in a specific domain are applied to a new domain but modified to suit the new environment, there will be a higher probability of applying the knowledge learned to other sections of the environment. This paper analyses the usefulness of transfer learning methods on air quality predictions, water quality and biodiversity prediction. The results showed that transfer learning has the potential to improve the predictive models performance even in cases when the training data is scarce. Nevertheless, one still has issues like heterogeneity of data and development of domain adaption techniques. The paper ends by mentioning some implications of such discoveries and future research directions to address current research limitations and advance the use of cross-domain machine learning in environmental monitoring any further.

Keywords: Crossdomain machine learning, transfer learning, environmental monitoring, predicted modeling, air quality, water quality, biodiversity.