Seismology is among the ancient sciences that concentrate on earthquake disaster
management (EQDM), which directly impact human life and infrastructure resilience. Such a …

This article presents a methodology to identify key studies using machine learning (ML) in
geophysics. We created a comprehensive database of fundamental articles for a systematic …

We use the acoustic emission (AE) and unsupervised machine learning to investigate the
influence of bedding structures on the tight rock fracturing at the microscale, aiming to …

Fractional-order PID (FOPID) controllers have gained increasing interest in control theory in
recent years, mainly to improve the performance and stability of complex systems. FOPID …

A great challenge of today's humanity is to serve the intensively increasing demand for food,
in an environmentally friendly manner. A promising way to address this problem is precision …

The current materials design methodologies depend on AI-driven approaches to accelerate
the development of materials. Deep learning (DL) and Machine Learning (ML) play essential …

Predicting flood hazard risk is crucial for reducing potential damage to infrastructures. This
study uses machine learning algorithms to improve the accuracy of flood hazard risk …

In this paper, we describe a hybrid clustering procedure which is well‐suited when we deal
with a large data set. It combines the K‐Means clustering to handle large data sets, and an …

Across various global regions abundant in oil and natural gas reserves, the presence of
substantial sub‐surface salt deposits holds significant relevance. Accurate identification of …

Understanding the failure process of tight reservoir rocks is essential for reservoir stimulation
with hydraulic fracturing. The acoustic emission (AE) technique has proven to be an effective …