TL;DR
The experimental findings indicate that the integrated GNN-GRU-MLP framework is effective in learning general Temporal relational representations, which show an overall classification accuracy of 97.25%, being balanced in the precision, recall, and F1-scores across all classes.
Reliable and up-to-date information on crop distribution is essential for ensuring food security in the face of rapid population growth and climate change, and with recent advancements in remote sensing and artificial intelligence, large-scale analyses of agricultural landscapes can now be performed more quickly and ac...
Accurate hyper-local climate prediction is essential for decision-making in agriculture, urban planning, and environmental management. This study addresses the scarcity of accessible monitoring solutions by developing an autonomous, IoT-enabled smart weather station that integrates high-frequency data acquisition with...
Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model, providing strong evidence of the value of combining DL methods for weather prediction.
Wastewater treatment processes generate highly dynamic time-series data characterized by strong nonlinearity and complex spatiotemporal dependencies, making the accurate prediction of effluent quality and energy consumption significantly challenging. To address these limitations in conventional single-structure network...
The scarcity of weather stations in many developing countries poses a significant obstacle to obtaining precise temperature data. Akwa Ibom State in Nigeria faces this challenge leading to reliance on satellite weather data (NASA POWER) and ground-truth measurements for temperature predictions. The study helps to predi...
Accurately monitoring lake water levels is critical for identifying potential hazards and developing sustainable water management strategies. While previous studies have primarily relied on single-source satellite data or traditional indices, this study enhances existing approaches by integrating multi-source satellite...
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