A review of using deep learning from an ecology perspective to address climate change and air pollution

Abstract

Deep learning, a unique class of artificial intelligence techniques that can shatter pattern recognition accuracy records, has recently attracted a lot of attention. With its flexibility and capacity to handle massive and complicated datasets, deep learning has transformed numerous academic domains, including bioinformatics and medicine, in a few of years. We think ecologists can also benefit from these methods, since ecological datasets are getting bigger and more complicated. This review examined current implementations and demonstrates how deep learning has been effectively applied to classify animal activity, identify species, and estimate biodiversity in big datasets such as audio recordings, videos, and camera-trap photos. This review paper show that most ecological disciplines, including applied contexts like management and conservation, can benefit from deep learning. This review also identify frequent problems concerning the application of deep learning, like what is the process for building a deep learning network, what resources are available, and what kind of data and processing power are needed. One of the biggest problems confronting humanity is climate change, and as deep learning (DL) specialists, you might be wondering how we can help. Here, we go over how machine learning (ML) can be an effective tool for cutting greenhouse gas emissions and assisting society in adjusting to a changing environment. We collaborate with various sectors to discover critical issues, such as disaster prevention and smart grids, where DL can bridge current gaps. This paper provides a thorough investigation of modelling using deep learning networks on actual air pollution data. With the support of this research, we hope to create deep learning air pollution structures in the future and improve the outcomes even more with knowledge from recent developments in deep learning research, including Generative Adversarial Networks (GANs), which pit two rival networks against one another to produce accurate data and forecast the state.