Aerobic Stress Detection in Aquatic Environments with Water Quality Data Using Hybrid Deep Learning Based ConvRec Model

Abstract

Depletion of dissolved oxygen in the water is a serious threat to fish and other aquatic organisms, it causes aerobic stress disease in fish. Detection of aerobic stress is crucial to maintain better growth and spawning in the fishes. Recently many studies proposed deep learning-based water quality analysis techniques, but these techniques inadequate in handling the complex water quality data. Because water quality has both spatial and temporal characteristics, this makes most of the deep learning models inadequate. To handle such complex and multifaceted data we proposed ConvRec, a deep learning architecture that incorporates CNN (Convolution neural network) and LSTM (Long-short term network) structures. CNN component extracts feature in the spatial domain from the water quality data from different locations while LSTM captures temporal features hence the model can learn both spatial and temporal correlations between the movement of water quality parameters to classify the aerobic stress in aqua ponds. In this work we use the two dataset both are unlabelled collected using IoT (Internet of things) devices. To handle this data using ConvRec model, usus the  fine-grained annotation of data points that have the effect of empowering the model to detect relevant traits associated with oxygen stress in fish. It can be therefore ascertained that ConvRec yields high degrees of accuracy of 99.2% and 99.65%, on the “ponds” and “waterx” datasets respectively while the past models can only 98.2% and 98.1% respectively on the same datasets. These results demonstrate that ConvRec is not only promising for estimating the health of fish during oxygen deficiency but also it can take part in reducing the negative impact of low oxygen levels in the water on fish.