Accurate prediction of lake water level is of great significance for flood prevention, reservoir
scheduling, and ecological protection. However, the change in lake water level is influenced …

This paper presents a solar cell crack detection system for use in photovoltaic (PV) assembly
units. The system utilizes four different Convolutional Neural Network (CNN) architectures …

Detection of cracks in solar photovoltaic (PV) modules is crucial for optimal performance and
long-term reliability. The development of convolutional neural networks (CNNs) has …

As the global demand for sustainable buildings continues to rise, accurately assessing and
managing carbon emissions during the operational phase of buildings has become an …

In recent years, deep learning (DL) approaches have been proven effective in addressing
high nonlinear relationships within complex systems. Although various scientific studies …

Rising sea temperatures and shifting tidal patterns, fuelled by climate change, cause
formidable threats to marine ecosystems. Accurate prediction of sea surface temperature …

The significance of short-term power load forecasting extends to grid dispatching and future
planning. To address the issues of nonlinear characteristics and poor prediction accuracy of …

This research evaluates the performance of deep learning (DL) models in predicting rainfall
in George Town, Penang, utilizing the open-source NASA POWER meteorological data …

The prediction of tidal fields is crucial in coastal and marine hydrodynamic analyses,
particularly in complex tidal environments, as it plays an essential role in disaster warning …

Coastal recession resulting from sea level rise and wave action is a significant
environmental concern, posing challenges for accurate long-term predictions due to the …