A Sensors-Based River Water Quality Assessment System Using Deep Neural Network

With the availability of low-cost and low-power sensors, it becomes easier to assess river water quality. The existing work on water quality assessment require a large amount of correctly annotated data for training. However, in the real-world scenario, obtaining such annotated data is costly and time consuming. In this work, we propose a sensor-based river water quality assessment system using the deep neural network (DNN). The system first presents a technique to estimate the water quality index (WQI) for labeling the given lab samples. WQI is a vital matrix used to transform large quantities of water data into a single unified number. Next, we present an automatic annotation technique that assigns labels to the sensory data instances using lab data. Finally, the labeled sensory data instances are used to build a DNN classifier that predicts water quality. This work also proposes a noise handling loss function to accommodate noisy labels. We evaluate the performance of the system on the river data set of major Indian rivers. We use four performance metrics during the experiment, including precision, recall, accuracy, and $F1$ score. Additionally, the system achieves an accuracy of more than 90%, despite 20% noisy labels. The code is at https://github.com/sourcecodecselab/river_water_monitoring.