  The increasing prevalence of marine pollution during the past few decades
motivated recent research to help ease the situation. Typical water quality
assessment requires continuous monitoring of water and sediments at remote
locations with labour intensive laboratory tests to determine the degree of
pollution. We propose an automated framework where we formalise a predictive
model using Machine Learning to infer the water quality and level of pollution
using collected water and sediments samples. One commonly encountered
difficulty performing statistical analysis with water and sediment is the
limited amount of data samples and incomplete dataset due to the sparsity of
sample collection location. To this end, we performed extensive investigation
on various data imputation methods' performance in water and sediment datasets
with various data missing rates. Empirically, we show that our best model
archives an accuracy of 75% after accounting for 57% of missing data.
Experimentally, we show that our model would assist in assessing water quality
screening based on possibly incomplete real-world data.
