This study investigated the importance of applying explainable artificial intelligence (XAI) on
different machine learning (ML) models developed to predict the strength characteristics of …

In this study, we used four different machine learning models-artificial neural network (ANN),
support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF)-to …

Climate change is a serious global issue causing more extreme weather patterns, resulting
in more frequent and severe events like urban flooding. This review explores the connection …

Streamflow forecasting is essential for effective water resource planning and early warning
systems. Streamflow and related parameters are often characterized by uncertainties and …

This study introduces the first-ever self-explanatory interface for diagnosing diabetes
patients using machine learning. We propose four classification models (Decision Tree (DT) …

Streamflow forecasting is crucial for effective water resource planning and early warning
systems, especially in regions with complex hydrological behaviors and uncertainties. While …

Abstract Machine learning (ML) as a subset of artificial intelligence (AI), has gained
significant attention in wind engineering applications over the past decade. Wind load …

Addressing the global challenge of traffic crashes necessitates transcending traditional
statistical models, which often fail to fully capture the interactions between factors causing …

This research utilized machine learning to analyze experiments conducted in an open
channel laboratory setting to predict microplastic transport with varying discharge, velocity …

Respiration plays a crucial role in the physiological development of embryos throughout the
entire incubation process and serves as a vital indicator for discerning unfertilized eggs and …