# A Machine Learning Predictive Model to Detect Water Quality and Pollution
## 2 Related Works  
Heavy metal pollution has brought an unpredictable threat to aquatic ecosystems as an increasing population and industrialization expanded. With the rapid change in urbanization, the concentration of heavy metals is serious in sewage wastewater, industrial wastewater discharges, and atmospheric deposition [1; 5]. Heavy metal concentrations in soil, water, and sediments are becoming severe due to intensive human activities [6]. According to [PERSON]', research on the comparison between heavy metal elements, excessive levels of heavy metals (such as Fe, Cu, Zn, Co, Mn, Se, and Ni) tend to be harmful to marine or marine life; other metals (such as Ag, Hg, and Pb) are fatal to marine organisms [7]. Therefore, it is particularly important to have an automatic framework that can quickly detect water quality. However, most of the current studies on water pollution assessment still use traditional methods. Traditional approaches, such as geochemical methods like inductively coupled plasma mass spectrometry (ICP-MS), require a labour-intensive process where it is time-consuming and high in cost [5; 8]. Moreover, these methods are not suitable when the test scale is substantially significant [9]. In search of the ability to detect contamination in different areas, one possible approach is to combine multiple sources of contamination datasets in a meaningful way. Utilizing multiple sources of information can enhance machine learning models' predictive capability and tackle the typical data scarcity issue with water sediment datasets. machine learning models can explore correlations between various variables more effectively and, thus, make more accurate predictions. For example, an artificial neural network (ANN) can classify images or recognize speech when conducting biological research [10; 11]. When there exists a mismatch of data features between datasets, data imputation can tackle the problem of missing data [11]. Some research works have already employed machine learning models to predict marine water quality. For example, [PERSON] et al. [12] have implemented a water quality prediction model using the XGBoost algorithm. BPNN, SVR, and LSTM models are applied in [13] to predict water quality which showed significant improvements. [PERSON]. [14] proposed a water quality prediction model combining improved grey regression analysis and LSTM. Interested readers can view more on water quality prediction using machine learning in the detailed review completed by [15]. Nevertheless, these studies on water quality prediction using machine learning hardly consider how to solve the problem of typical data scarcity of water and sediment datasets.  
In addition, many water quality assessment study used environmental indexes which are some of the established standards to deduce the degree of water contamination [3]. However, multiple standards exist, such as geo-accumulation index (Igeo), and enrichment factor (EF). However, none are considered the \"golden standard\". Therefore, the combination of dataset and environmental indexes is a possible approach to combine the well-established environmental index with the predictive capability of machine learning.