Globally, over 17 million people annually die from cardiovascular diseases, with heart
disease being the leading cause of mortality in the United States. The ever-increasing …

The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic
domain is pivotal for addressing the multifaceted challenges posed by climate change on …

Estimating gross primary productivity (GPP) over space and time is fundamental for
understanding the response of the terrestrial biosphere to climate change. Eddy covariance …

Automated machine learning (AutoML), which explores the automation of various machine
learning tasks, has seen significant progress in recent years. One of the fundamental tasks …

This paper presents a comprehensive exploration of automatic machine learning (AutoML)
tools in the context of classification and regression tasks. The focus lies on understanding …

Flooding poses a severe threat to communities and infrastructure worldwide, which requires
advanced flood forecasting warning systems. In this research paper, a real-time flood …

In this study, an ensemble inflow-prediction system was developed for a hydropower-
generation dam in the upper Sai River basin, and the accuracy of ensemble inflow …

Reliable streamflow forecasting is crucial for several tasks related to water-resource
management, including planning reservoir operations, power generation via Hydroelectric …