Genetic programming in water resources engineering: A state-of-the-art review
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …
automatic generation of computer programs. In recent decades, GP has been frequently …
Development of a hybrid computational intelligent model for daily global solar radiation prediction
Providing an accurate and reliable solar radiation prediction is highly significant for optimal
design and management of thermal and solar photovoltaic systems. It is massively essential …
design and management of thermal and solar photovoltaic systems. It is massively essential …
Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model
Accurate prediction of compressive strength (fc) is one of the crucial problems in the
concrete industry. In this study, novel self-adaptive and formula-based model called …
concrete industry. In this study, novel self-adaptive and formula-based model called …
Effect of river flow on the quality of estuarine and coastal waters using machine learning models
MJ Alizadeh, MR Kavianpour, M Danesh… - Engineering …, 2018 - Taylor & Francis
This study explores the river-flow-induced impacts on the performance of machine learning
models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay …
models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay …
[PDF][PDF] Prediction of the compressive strength of self-compacting concrete using surrogate models
In this paper, surrogate models such as multivariate adaptive regression splines (MARS)
and M5P model tree (M5P MT) methods have been investigated in order to propose a new …
and M5P model tree (M5P MT) methods have been investigated in order to propose a new …
Extreme learning machine model for water network management
A novel failure rate prediction model is developed by the extreme learning machine (ELM) to
provide key information needed for optimum ongoing maintenance/rehabilitation of a water …
provide key information needed for optimum ongoing maintenance/rehabilitation of a water …
[HTML][HTML] Covariance matrix adaptation evolution strategy for improving machine learning approaches in streamflow prediction
Precise streamflow estimation plays a key role in optimal water resource use, reservoirs
operations, and designing and planning future hydropower projects. Machine learning …
operations, and designing and planning future hydropower projects. Machine learning …
Hybrid machine learning models for estimating total organic carbon from mineral constituents in core samples of shale gas fields
CM Saporetti, DL Fonseca, LC Oliveira… - Marine and Petroleum …, 2022 - Elsevier
The analysis of total organic carbon (TOC) contents is an important activity in exploring
potentially hydrocarbon-generating intervals. Petroleum source rocks have, by definition …
potentially hydrocarbon-generating intervals. Petroleum source rocks have, by definition …
Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models
M Najafzadeh, M Rezaie Balf… - Journal of …, 2016 - iwaponline.com
Pier scour phenomena in the presence of debris accumulation have attracted the attention of
engineers to present a precise prediction of the local scour depth. Most experimental studies …
engineers to present a precise prediction of the local scour depth. Most experimental studies …
Hourly road pavement surface temperature forecasting using deep learning models
Road authorities in cold climates regularly apply salt on roads, during winter, to ensure
public safety. Pavement surface temperature is a significant parameter affecting snow and …
public safety. Pavement surface temperature is a significant parameter affecting snow and …