Recent advancements in neural network methodologies have revolutionised hydrological forecasting, enabling more accurate, robust and computationally efficient predictions of water resource dynamics.

Increasingly, AI models are able make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...

In the AI era, pure data-driven meteorological and climate models are gradually catching up with and even surpassing traditional numerical models. However, significant challenges persist in current ...

Can a handful of atoms outperform a much larger digital neural network on a real-world task? The answer may be yes. In a ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...

Read more about Deep learning and AI unlock new era of solar energy forecasting and performance on Devdiscourse ...

We propose a new machine-learning-based approach for forecasting Value-at-Risk (VaR) named CoFiE-NN where a neural network (NN) is combined with Cornish-Fisher expansions (CoFiE). CoFiE-NN can capture ...

In the AI era, pure data-driven meteorological and climate models are gradually catching up with, and even surpassing, traditional numerical models. However, significant challenges persist in current ...