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Accurate weather forecasting is increasingly crucial as climate change intensifies the unpredictability of weather patterns, posing challenges to traditional forecasting models reliant on human observation or numerical methods. Researchers are working on precise weather forecasting to improve our preparedness, enabling fast response to any disaster. Among other techniques, deep learning is a prudent method to predict weather forecasts since it can automatically learn and train from a vast amount of data to generate and portray accurate features of an incident. This study evaluates deep learning techniques for weather forecasting based on different meteorological characteristics. This paper examines a few weather variables to evaluate the prediction performance of several deep learning solutions using TensorFlow and pre-trained Keras applications models. For this purpose, the top ten accuracy-based deep learning model architectures have been investigated and evaluated. The operation of each model is distinct. Models like EfficientNetB7, ResNet, MobileNet, VGG19, Xception Inception, ResNetV2, and VGG16 employ a combination of image classification and deep learning models to predict the weather. The WEAPD dataset of 6877 images representing 11 weather phenomena categories was utilized, and the models were trained and validated using an 80:10:10 split. Predictions, extraction of features, and fine-tuning of models were achieved with an accuracy of up to 83.39%. Most models performed well in image classification, enhancing the proposed framework and achieving significant precision in generating weather photos and reports.

The conversion of waste heat into power is important due to the diminishing reserviors of fossil fuel and the rise in environmental pollution. The recovery of waste heat from internal combustion (IC) engines through the Organic Rankine Cycle (ORC) presents a practical approach to improving overall performance, attributable to the simple design of the ORC system. This study examines the energy, exergy, economic, and environmental aspects of IC engine waste heat-driven basic and regenerative configurations of the Organic Rankine cycle. The chosen configurations of ORCs are combined with reverse osmosis (RO) units to produce freshwater. The ORCs use R245fa as the working fluid. The results indicate that the maximum net power output, energy efficiency, exergy efficiency, rate of exergy destruction, working fluid mass flow rate, and distillateflow rate obtained from the basic ORC(BORC) were :195.9 kW, 14.68 %, 19.65 %, 732.7 kW, 5.499 kg/s, and 19.74 m³/h, respectively. Moreover, the maximum net power output, energy efficiency, energy and exergy efficiencies, rate of exergy destruction, working fluid mass flow rate, and distillate flow rate obtained from theregenerative ORC(RORC) were evaluated as 200.9 kW, 15.06 %, 20.16, 730.3 kW, 5.642 kg/s, and 20.21 m³/h, respectively. The optimal payback period is indicated by BORC, calculated to be 2.87 years. The RORC demonstrates enhanced sustainability. The maximum sustainability index determined was 1.34 for RORC. The parametric analysis reveals that increasing turbine inlet pressure (TIP) is evaluated to enhance system performance. However, increase in Condenser Pressure (CP) negatively impacts system performance. RORC's performance surpasses BORC's, owing to its regenerative capabilities.