Amina Said

Optical properties of water provide information about the condition of water bodies which can also give information on water pollution. Various water quality parameters such as the total suspended matter (TSM), chlorophyll and colour dissolved organic matter (CDOM) concentrations were traditionally based on estimations from in situ data. This method is expensive, time-consuming and provided local coverage. However, technology has been progressively improving and advancing. More remote sensing data and platforms are now freely and easily accessible. Water quality can now be extracted from the satellite imageries with relatively good spatial resolution, data is more frequently collected at a global coverage and at a good accuracy. A strong positive correlation coexists between the in situ data and satellite imageries on water properties. Though, machine learning can assist in further improving the accuracy and automate the process by using trained models. The size of dataset tends to influence the accuracy of remote sensing results. Training a model in machine learning requires a large dataset which eventually improves the accuracy of remote sensing. The process mainly involves preparing the data for training in a model. A suitable machine learning model is selected and model is fit into the data. It involves learning the pattern of the data and using the pattern to make predictions. Next, the model is evaluated to assess the accuracy and precision of the model. If the accuracy of the model is low, tuning and experimentation is carried out. Which improves the accuracy of the model by manipulating the hyperparameters and comparing accuracy of the model from the various hyperparameters used. The model is saved once the required accuracy is acquired. This can then be reloaded and reused for making predictions and classification. Which can assist in making prediction on water quality parameters, classification of satellite imageries into respective feature classes or detect specific features in an imagery. Thus, machine learning assists in predictions of both in situ and satellite data, classification and feature detection which ultimately saves time, money and improves the accuracy. However, various challenges are expected in this research project. Such as finding satellite imageries with good spatial resolution and with less than 10 percent of cloud cover. Moreover, training the model and improving its accuracy can be time-consuming. Despite the challenges, the research project will provide an insight into the potential of remote sensing and machine learning in extracting the water optical properties