Estimation of rubberized concrete frost resistance using machine learning techniques
Utilizing waste rubber in concrete has effectively reduced global environmental pollution
and carbon emission. It is essential to accurately evaluate and predict the evolution of its …
and carbon emission. It is essential to accurately evaluate and predict the evolution of its …
A novel hybrid adaptive boosting approach for evaluating properties of sustainable materials: A case of concrete containing waste foundry sand
Ensemble learning (EL) has gained popularity in recent investigations because of its higher
prediction accuracy than conventional machine learning (ML) methods. Regressors and EL …
prediction accuracy than conventional machine learning (ML) methods. Regressors and EL …
Data-driven models for atmospheric air temperature forecasting at a continental climate region
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence
on multiple fields such as hydrology, the environment, irrigation, and agriculture, this …
on multiple fields such as hydrology, the environment, irrigation, and agriculture, this …
Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm
Abstract Fine particulate matter (PM2. 5) is a significant air pollutant that drives the most
chronic health problems and premature mortality in big metropolitans such as Delhi. In such …
chronic health problems and premature mortality in big metropolitans such as Delhi. In such …
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture,
and ecosystems. However, climate change has worsened droughts, leading to significant …
and ecosystems. However, climate change has worsened droughts, leading to significant …
Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization …
MM Hameed, SF Mohd Razali… - … Research and Risk …, 2023 - Springer
Climate change has increased drought frequency globally, which harms the environment,
agriculture, and water resources. This study explores the capacity of a hybrid model based …
agriculture, and water resources. This study explores the capacity of a hybrid model based …
Shear modulus prediction of landfill components using novel machine learners hybridized with forensic-based investigation optimization
The assessment of the shear modulus (G) of municipal solid waste (MSW) and leachate-
contaminated soil (LCS) is of vital importance for landfill engineering investigation and …
contaminated soil (LCS) is of vital importance for landfill engineering investigation and …
Optimising the selection of input variables to increase the predicting accuracy of shear strength for deep beams
The deep beam in load transfer is very important as well as difficult to design due to its shear
stress problems. Accurate estimation of shear stress would help engineers to get a safer …
stress problems. Accurate estimation of shear stress would help engineers to get a safer …
The influence of data length on the performance of artificial intelligence models in predicting air pollution
Air pollution is one of humanity's most critical environmental issues and is considered
contentious in several countries worldwide. As a result, accurate prediction is critical in …
contentious in several countries worldwide. As a result, accurate prediction is critical in …
Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures
Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated
soil deposits, resulting in catastrophic ground failure. Accurate prediction of such …
soil deposits, resulting in catastrophic ground failure. Accurate prediction of such …