In the realm of data science and machine learning, time series analysis plays a crucial role in examining and predicting data that evolves over time. These sequential observations, recorded at regular intervals, hold significant value in comprehending various phenomena, including environmental dynamics. The Mar Menor, situated in the Region of Murcia, presents a particularly urgent case due to its unique ecosystem and the challenges it confronts. This paper addresses the imperative need to investigate the environmental parameters of the Mar Menor and develop accurate forecasting models for informed decision-making and environmental management. These parameters, encompassing water quality, temperature, salinity, nutrient levels, chlorophyil, and more, exhibit intricate temporal patterns influenced by a multitude of factors, including human activities, climate change, and natural processes. By leveraging advanced machine learning techniques, we can reveal valuable insights into their behavior and project future trends, empowering stakeholders to implement effective strategies for conservation and sustainable development.
The approach undertaken in this study encompasses both descriptive and predictive analyses, aiming to identify, on one hand, a methodology for time series analysis that suits each dataset based on its specific characteristics in general, and on the other hand, at a more specific level, to discover the most suitable predictive model for time series forecasting based on the unique characteristics of the Mar Menor dataset. This includes identifying potential trends, seasonality, and temporal dependencies that contribute to the complexity of the environmental parameters. To find the most appropriate predictive model, a series classification is performed using robust time series analysis methods such as correlation analysis or the Dickey-Fuller hypothesis, along with evaluation and comparison techniques like the Akaike (AIC) and Bayesian (BIC) information criteria, which allow finding the model that best fits the series' characteristics.
Several state-of-the-art machine learning algorithms and statistical models, such as autoregressive models (AR), moving average models (MA), or the Facebook Prophet model, as well as recurrent neural networks (RNN), such as long short-term memory (LSTM), are thoroughly investigated to assess their efficiency in capturing the intricate dynamics of the Mar Menor's environmental parameters. The metrics RMSE, MAE, and MAPE will determine how well these models fit the Mar Menor series.
The ecosystem of this fragile lagoon is susceptible to pollution, overfishing, and the impacts of climate change, necessitating a comprehensive understanding of the underlying processes to ensure its preservation. By developing accurate and robust forecasting models, this study aims to facilitate the identification of critical periods, evaluate potential risks, and formulate proactive mitigation strategies that can aid in maintaining the health and resilience of the Mar Menor's ecosystem.
The results demonstrate that both statistical models and artificial intelligence models are entirely valid for handling the time series of the Mar Menor. However, there is high variation in the accuracy of these models depending on the model configuration. In statistical models, SARIMA was the best model for most datasets. For example, for the temperature, chlorophyll, and oxygen datasets, this model achieves an RMSE of 0.33, 0.63, and 0.025, respectively. On the other hand, in linear models, the support vector machine with a linear configuration is the best in two parameters, with a temperature RMSE of 0.37 and a chlorophyll RMSE of 0.82.
