Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India

Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data collected from India Meteorological Department in northeast region over a period of 118 years. We conducted a comparative analysis of these methods to determine their relative effectiveness in predicting rainfall patterns. Using historical rainfall data from multiple weather stations, we trained and validated our models to forecast future rainfall patterns. Our results indicate that both DMD and LSTM are effective in forecasting rainfall, with LSTM outperforming DMD in terms of accuracy, revealing that LSTM has the ability to capture complex nonlinear relationships in the data, making it a powerful tool for rainfall forecasting. Our findings suggest that data-driven methods such as DMD and deep learning approaches like LSTM can significantly improve rainfall forecasting accuracy in the North-East region of India, helping to mitigate the impact of extreme weather events and enhance the region's resilience to climate change.Comment: Paper is under review at ICMC 202

EFFECT OF HYPERPARAMETERS ON DEEPLABV3+ PERFORMANCE TO SEGMENT WATER BODIES IN RGB IMAGES

Deep Learning (DL) networks used in image segmentation tasks must be trained with input images and corresponding masks that identify target features in them. DL networks learn by iteratively adjusting the weights of interconnected layers using backpropagation, a process that involves calculating gradients and minimizing a loss function. This allows the network to learn patterns and relationships in the data, enabling it to make predictions or classifications on new, unseen data. Training any DL network requires specifying values of the hyperparameters such as input image size, batch size, and number of epochs among others. Failure to specify optimal values for the parameters will increase the training time or result in incomplete learning. The rationale of this study was to evaluate the effect of input image and batch sizes on the performance of DeepLabV3+ using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We trained DeepLabV3+ network six times with two sets of input images of 128 × 128-pixel, and 256 × 256-pixel dimensions with 4, 8 and 16 batch sizes. The model is trained for 100 epochs to ensure that the loss plot reaches saturation and the model converged to a stable solution. Predicted masks generated by each model were compared to their corresponding test mask images based on accuracy, precision, recall and F1 scores. Results from this study demonstrated that image size of 256 × 256 and batch size 4 achieved highest performance. It can also be inferred that larger input image size improved DeepLabV3+ model performance

Combatting infectious diarrhea: innovations in treatment and vaccination strategies

ABSTRACTIntroduction The escalating prevalence of infectious diseases is an important cause of concern in society. Particularly in several developing countries, infectious diarrhea poses a major problem, with a high fatality rate, especially among young children. The condition is divided into four classes, namely, acute diarrhea, invasive diarrhea, acute bloody diarrhea, and chronic diarrhea. Various pathogenic agents, such as bacteria, viruses, protozoans, and helminths, contribute to the onset of this condition.Areas covered The review discusses the scenario of infectious diarrhea, the prevalent types, as well as approaches to management including preventive, therapeutic, and vaccination strategies. The vaccination techniques are extensively discussed including the available vaccines, their advantages as well as limitations.Expert opinion There are several approaches available to develop new-improved vaccines. In addition, route of immunization is important and aerosols/nasal sprays, oral route, skin patches, powders, and liquid jets to minimize needles can be used. Plant-based vaccines, such as rice, might save packing and refrigeration costs by being long-lasting, non-refrigerable, and immunogenic. Future research should utilize predetermined PCR testing intervals and symptom monitoring to identify persistent pathogens after therapy and symptom remission