AI forecast models offer some clear benefits over traditional physical models, but they are ill-equipped to handle the increasing volatility of a warming climate.

Automotive manufacturers are adopting predictive analytics, machine learning, and advanced inventory methods to align stock with market needs and cut waste. By analyzing historical demand trends, ...

AI-driven forecasting and optimization are transforming the automotive supply chain, improving demand accuracy, cutting inventory costs, and enhancing resilience. Studies highlight gains from ensemble ...

This Research Topic highlights recent advances in fractal geometry, fractional calculus, and artificial intelligence (AI) for addressing complex problems in ...

A new satellite-based analytical framework enables accurate estimation of crop sowing and emergence dates at the field scale.

Researchers have developed a feature selection-based solar irradiance forecasting method to improve the operation of ...

A new wave of intelligent water systems powered by Artificial Intelligence of Things (AIoT) is transforming how governments and industries monitor, predict, and manage water resources, as mounting ...

Trained on historical consumption data spanning a decade, the model demonstrated strong predictive performance. It achieved a training error of 0.182 and a forecasting accuracy of 95.2 percent, ...

ABSTRACT: Cereal production in Somalia is characterized by extreme volatility driven by climate shocks. This study addresses the limitations of traditional agricultural planning by evaluating optimal ...

Inflation, labor shortages, and rising operating costs have become persistent challenges for businesses and communities across the United States. Yet many of the decisions that shape these ...