Highlights Coronavirus disease 2019 (COVID-19) has taken a heavy toll on human life. Creating a hybrid model that efficiently sanitizes the mask and increases its reuse. The hybrid system can be used as standalone systems also.

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Sea surface temperature (SST) prediction has widespread applications in the field of marine ecology, fisheries, sports and climate change studies. At present, the real-time SST forecasts are made by numerical models which are categorically based on physics-based assumptions subjected to boundary and initial conditions. They are more suited to a large spatial region than in a specific location. In this study, location-specific SST forecasts were made by combining deep learning neural networks with numerical estimators at five different locations around India for three different time horizons (daily, weekly and monthly). Firstly, forecasts were made with traditional neural networks (NNs) and then through deep learning networks. The NNs significantly improved on the results achieved by numerical forecasts which were further enhanced by the deep learning long short-term memory (LSTM) neural network over all timescales and at all the selected sites. The model was performed successfully in terms of various statistical parameters with correlation values nearing 1.0 while minimizing the errors. Additionally, a comparative study with a linear system, the autoregressive integrated moving average with exogenous input was made. The predictive skills of deep learning LSTMs are found to be more attractive than the other existing techniques (linear or other NNs) due to their ability of learning long time dependencies and extracting features from a sample space.

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