Abstract:
Machine learning (ML) is revolutionizing the field of aquatic environment research by
offering advanced tools for analyzing, classifying, and predicting data. This study delves
into the use of ML algorithms, particularly Decision Trees, Random Forests, and
XGBoost, for assessing water quality across various contexts such as surface water,
groundwater, drinking water, and wastewater. These ML models excel in handling the
increasing complexity and volume of data in water research, surpassing the capabilities of
traditional models. In this work, I explored the application of ML in several key areas:
monitoring and simulation of water systems, evaluation, and optimization of water
treatment processes, and addressing challenges like water pollution and watershed
security. The ability of ML models to process data from diverse sensors and monitoring
systems in real-time makes them invaluable for understanding water quality parameters
and identifying potential risks. The predictive power of ML is particularly noteworthy in
forecasting changes in water quality due to environmental factors, which is critical for
proactive water management and policymaking. Furthermore, the study highlights how
ML aids in optimizing water treatment processes, leading to more efficient and
sustainable operations. Looking ahead, the study discusses the potential future
applications of ML in the aquatic domain. This includes the integration of deep learning
methods for more nuanced analyses, improved handling of data variability and
uncertainty, and the combination of ML with other emerging technologies such as IoT,
blockchain, and cloud computing. This synergy is poised to enhance water resource
management, emphasizing sustainability, accessibility, and conservation. In summary,
this work presents a comprehensive overview of how ML algorithms are transforming the
landscape of water environment research, offering innovative solutions for current
challenges, and opening new avenues for future exploration
