# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
###### Abstract  
Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span of two decades. Leveraging the power of Machine Learning (ML) algorithms, we developed models that not only predict, but also provide insights into, the intricate relationships between various water-quality parameters. Our findings indicate a significant potential in using ML techniques, especially when dealing with complex environmental datasets. The ML methods employed in this study are grounded in both statistical and computational principles, ensuring robustness and reliability in their predictions. The significance of our research lies in its ability to provide timely and accurate forecasts, aiding in proactive water-management strategies. Furthermore, we delve into the potential explanations behind the success of our ML models, emphasizing their capability to capture non-linear relationships and intricate patterns in the data, which traditional models might overlook. urban lake; water quality; machine learning; prediction; regression; neural networks; random forest +
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these pioneering works, employing advanced ML algorithms to predict the Water Quality Index (WQI) of an urban lake over two decades. Water is an essential resource to all life forms, including human beings. It plays a crucial role in the economic, social, and environmental aspects of our planet. As human activities intensify, particularly in urban residential areas, water bodies such as lakes, rivers, and groundwater aquifers have become increasingly subject to pollution. Of particular concern is the degradation of water quality in urban lakes, which serve as crucial reservoirs for biodiversity, recreation, and, sometimes, even drinking water supplies [3; 4; 5]. The water quality of urban lakes is a complex issue, deeply intertwined with environmental and anthropogenic factors. Residential catchment areas pose significant threats to water quality due to the potential for domestic waste and sewage runoff, chemical pollutants from household activities, and stormwater overflow. The implications of these activities are profound, affecting the ecological health of the water bodies, their use for recreational activities, and their potential as water sources [6; 7; 8]. Traditionally, water-quality assessment and prediction have been conducted through manual sampling and laboratory analysis, coupled with statistical models to understand trends and forecast future changes. However, these methods have significant drawbacks: they are time-consuming, labor-intensive, and may fail to capture the complex, nonlinear relationships between various water-quality parameters. Moreover, these conventional techniques struggle to accommodate the rapid changes in urban landscapes, making it challenging to accurately predict future water-quality states [9; 10; 11]. In response to these challenges, this study explores the application of Machine Learning (ML) techniques for predicting water quality in an urban lake situated in a residential catchment area. Over the past two decades, ML has emerged as a powerful tool for modeling complex, high-dimensional datasets. In environmental sciences, ML algorithms have been successfully applied in areas like climate modeling, soil analysis, and air-quality forecasting. They have shown promising results due to their ability to learn intricate patterns from data, and deal with multi-source and heterogeneous data, and their capacity for prediction [12; 13; 14]. This research harnesses the potential of ML techniques to understand and predict the water quality of an urban lake based on 20 years of collected data. Specifically, we employ a combination of Regression Models, Neural Networks, and Ensemble Methods. Regression models, although simple, offer a strong interpretability of the relationships between variables.