A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction-Reference-Cited by-同舟云学术

A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction

Published:2022-09 Issue: Volume:114 Page:105150
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Ma Junwei^ORCID,Xia Ding,Wang Yankun,Niu Xiaoxu,Jiang Sheng,Liu Zhiyang,Guo Haixiang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference114 articles.

1. Link-based multi-verse optimizer for text documents clustering;Abasi;Appl. Soft Comput. J.,2020

2. The arithmetic optimization algorithm;Abualigah;Comput. Methods Appl. Mech. Engrg.,2021

3. Neural network based unified particle swarm optimization for prediction of asphaltene precipitation;Ahmadi;Fluid Phase Equilib.,2012

4. Developing a robust surrogate model of chemical flooding based on the artificial neural network for enhanced oil recovery implications;Ahmadi;Math. Probl. Eng.,2015

5. A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature;Ahmadi;Fuel,2015

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