Flood prediction using machine learning models: Literature review
Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …
The research on the advancement of flood prediction models contributed to risk reduction …
Artificial neural networks in drought prediction in the 21st century–A scientometric analysis
Droughts are the most spatially complex geohazard, which often lasts for years, thereby
severely impacting socio-economic sectors. One of the critical aspects of drought studies is …
severely impacting socio-economic sectors. One of the critical aspects of drought studies is …
Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques
IMK Ho, KY Cheong, A Weldon - Plos one, 2021 - journals.plos.org
Despite the wide adoption of emergency remote learning (ERL) in higher education during
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
J Duan, PG Asteris, H Nguyen, XN Bui… - Engineering with …, 2021 - Springer
Recycled aggregate concrete is used as an alternative material in construction engineering,
aiming to environmental protection and sustainable development. However, the …
aiming to environmental protection and sustainable development. However, the …
[HTML][HTML] Water quality prediction and classification based on principal component regression and gradient boosting classifier approach
Estimating water quality has been one of the significant challenges faced by the world in
recent decades. This paper presents a water quality prediction model utilizing the principal …
recent decades. This paper presents a water quality prediction model utilizing the principal …
BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
Breast cancer is one of the most commonly diagnosed cancer types in the woman and
automatically classifying breast cancer histopathological images is an important task in …
automatically classifying breast cancer histopathological images is an important task in …
Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system
Hydropower is among the cleanest sources of energy. However, the rate of hydropower
generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey …
generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey …
Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings
A Moradzadeh, A Mansour-Saatloo… - Applied Sciences, 2020 - mdpi.com
Nowadays, since energy management of buildings contributes to the operation cost, many
efforts are made to optimize the energy consumption of buildings. In addition, the most …
efforts are made to optimize the energy consumption of buildings. In addition, the most …
Earth fissure hazard prediction using machine learning models
Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the
semi-arid basins. The excessive withdrawal of groundwater, as well as the other …
semi-arid basins. The excessive withdrawal of groundwater, as well as the other …
New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia
Ensuring availability in terms of quality and quantity and sustainable management of safe,
affordable drinking water is one of the integral parts of envisioning the 2030 Sustainable …
affordable drinking water is one of the integral parts of envisioning the 2030 Sustainable …