Time Series Modelling and Predictive Analytics for Sustainable Environmental Management—A Case Study in El Mar Menor (Spain) †

Abstract

In this study on data science and machine learning, time series analysis plays a key role in predicting evolving data patterns. The Mar Menor, located in the Region of Murcia, represents an urgent case due to its unique ecosystem and the challenges it faces. This paper highlights the need to study the environmental parameters of the Mar Menor and to develop accurate predictive models and a standardised methodology for time series analysis. These parameters, which include water quality, temperature, salinity, nutrients, chlorophyll, and others, show complex temporal variations influenced by different activities. Advanced time series models are used to gain insight into their behaviour and project future trends, facilitating effective conservation and sustainable development strategies. Models such as SARIMA and LSTM stand out as valid for predicting the environmental patterns of the Mar Menor.