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Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.

Water science and technology : a journal of the International Association on Water Pollution Research
Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle t...

Development and performance of a machine learning-based tool that predicts influent flow to a WRF 72 h in advance and integrates with existing wet weather nutrient management protocols.

Water environment research : a research publication of the Water Environment Federation
Influent flow to the 75 mgd Neuse River Resource Recovery Facility (NRRRF) was modeled using machine learning. The trained model can predict hourly flow 72 h in advance. This model was deployed in July 2020, and has been in operation over two and a h...

Deep learning for twelve hour precipitation forecasts.

Nature communications
Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasin...

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Sensors (Basel, Switzerland)
Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and suscept...

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods.

Sensors (Basel, Switzerland)
Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such...

Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data.

Scientific reports
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised mach...

ECMWF short-term prediction accuracy improvement by deep learning.

Scientific reports
This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main i...

Prediction of COVID-19 cases using the weather integrated deep learning approach for India.

Transboundary and emerging diseases
Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non-linear problems. In the pres...

Weather Classification by Utilizing Synthetic Data.

Sensors (Basel, Switzerland)
Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing location...

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources.

Sensors (Basel, Switzerland)
Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based app...