Flood prediction using machine learning models: Literature review

A Mosavi, P Ozturk, K Chau - Water, 2018 - mdpi.com
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 …

Artificial neural networks in drought prediction in the 21st century–A scientometric analysis

A Dikshit, B Pradhan, M Santosh - Applied Soft Computing, 2022 - Elsevier
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 …

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 …

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 …

[HTML][HTML] Water quality prediction and classification based on principal component regression and gradient boosting classifier approach

MSI Khan, N Islam, J Uddin, S Islam… - Journal of King Saud …, 2022 - Elsevier
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 …

BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer

M Toğaçar, KB Özkurt, B Ergen, Z Cömert - Physica A: Statistical Mechanics …, 2020 - Elsevier
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 …

Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system

M Dehghani, H Riahi-Madvar, F Hooshyaripor… - Energies, 2019 - mdpi.com
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 …

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 …

Earth fissure hazard prediction using machine learning models

B Choubin, A Mosavi, EH Alamdarloo, FS Hosseini… - Environmental …, 2019 - Elsevier
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 …

New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia

MS Manzar, M Benaafi, R Costache, O Alagha… - Ecological …, 2022 - Elsevier
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 …