Artificial Intelligence and Machine Learning-Based Prediction Models for Environmental Quality Assessment and Their Relevance to Daoist Ecology

Abstract

The assessment of environmental quality has become a very important and challenging task with the rapid increase of air pollution, water pollution, land use pollution, climate change and loss of biodiversity. Conventional methods for environmental monitoring have some constraints, such as high cost, time consuming, low spatial coverage, and difficulty to make real time predictions. However, the use of large datasets and environmental information by means of artificial intelligence and machine learning has made it possible to assess and predict the environmental quality with high accuracy. These methods include random forest, support vector machine, artificial neural networks, gradient boosting, long and short term memory networks, and also hybrid deep learning models. These models can be used to predict air quality, water quality, soil pollution, climate and ecological risk, and support early warning systems, pollution forecasting, resource management, and also evidence-based environmental management and decision making. However, the use of advanced environmental technologies should be based on an ethical and ecological framework. From this point of view, Daoist ecology can provide a suitable philosophy with nature harmonizing, balance, moderate, natural and non-forced actions. In this paper, the role of prediction models of environmental quality assessment by using artificial intelligence and machine learning will be discussed, and also their relevance to the principles of Daoist ecology will be examined. From the proposed perspective, environmental prediction models should not only focus on technical accuracy but also support sustainable, responsible and nature sensitive decisions.