Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review DOI Open Access
Mojtaba Zaresefat, Reza Derakhshani

Water, Journal Year: 2023, Volume and Issue: 15(9), P. 1750 - 1750

Published: May 2, 2023

Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial improving water resources planning management. In the past 20 years, significant progress has been made in management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances this field, existing literature must cover ML. This article aims to understand current state-of-the-art ML used achievements domain. It most cited employed from 2009 2022. summarises reviewed papers, highlighting their strengths weaknesses, performance criteria employed, highly identified. worth noting that accuracy was significantly enhanced, resulting a substantial improvement demonstrating robust outcome. Additionally, outlines recommendations future research directions enhance of including prediction related knowledge.

Language: Английский

Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review DOI
Haonan Guo, Shubiao Wu, Yingjie Tian

et al.

Bioresource Technology, Journal Year: 2020, Volume and Issue: 319, P. 124114 - 124114

Published: Sept. 11, 2020

Language: Английский

Citations

286
A review of machine learning in processing remote sensing data for mineral exploration DOI

Hojat Shirmard,

Ehsan Farahbakhsh, R. Dietmar Müller

et al.

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 268, P. 112750 - 112750

Published: Oct. 30, 2021

Language: Английский

Citations

237
U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow DOI
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli

et al.

Advances in Water Resources, Journal Year: 2022, Volume and Issue: 163, P. 104180 - 104180

Published: April 5, 2022

Language: Английский

Citations

214
Deep learning in pore scale imaging and modeling DOI
Ying Da Wang, Martin J. Blunt, Ryan T. Armstrong

et al.

Earth-Science Reviews, Journal Year: 2021, Volume and Issue: 215, P. 103555 - 103555

Published: Feb. 10, 2021

Language: Английский

Citations

191
Machine learning for hydrologic sciences: An introductory overview DOI
Tianfang Xu, Feng Liang

Wiley Interdisciplinary Reviews Water, Journal Year: 2021, Volume and Issue: 8(5)

Published: May 27, 2021

Abstract The hydrologic community has experienced a surge in interest machine learning recent years. This is primarily driven by rapidly growing data repositories, as well success of various academic and commercial applications, now possible due to increasing accessibility enabling hardware software. overview intended for readers new the field learning. It provides non‐technical introduction, placed within historical context, commonly used algorithms deep architectures. Applications sciences are summarized next, with focus on studies. They include detection patterns events such land use change, approximation variables processes rainfall‐runoff modeling, mining relationships among identifying controlling factors. also discussed context integrated process‐based modeling parameterization, surrogate bias correction. Finally, article highlights challenges extrapolating robustness, physical interpretability, small sample size applications. categorized under: Science Water

Language: Английский

Citations

150
A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet DOI
Yanrui Ning, Hossein Kazemi, Pejman Tahmasebi

et al.

Computers & Geosciences, Journal Year: 2022, Volume and Issue: 164, P. 105126 - 105126

Published: May 6, 2022

Language: Английский

Citations

131
Big Data in Earth system science and progress towards a digital twin DOI
Xin Li, Min Feng, Youhua Ran

et al.

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(5), P. 319 - 332

Published: May 2, 2023

Language: Английский

Citations

124
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues DOI
Shady E. Ahmed, Suraj Pawar, Omer San

et al.

Physics of Fluids, Journal Year: 2021, Volume and Issue: 33(9)

Published: Sept. 1, 2021

For over a century, reduced order models (ROMs) have been fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin inspired by the Orr–Sommerfeld stability equation and numerous vortex models, which von Kármán street is one most prominent. Subsequent ROMs typically relied on first principles, like mathematical weakly nonlinear theory, two- three-dimensional models. Aubry et al. [J. Fluid Mech. 192, 115–173 (1988)] pioneered data-driven proper orthogonal decomposition (POD) modeling. In early POD modeling, available data were used to build an optimal basis, was then utilized in classical procedure construct ROM, but made profound impact beyond expansion. this paper, we take modest step illustrate modeling significant ROM area. Specifically, focus closures, are correction terms that added model effect discarded modes under-resolved simulations. Through simple examples, main principles ROMs, motivate introduce modern show how artificial intelligence, machine learning changed standard methodology last two decades. Finally, outline our vision state-of-the-art can continue reshape field

Language: Английский

Citations

122
The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning DOI
Nawal Taoufik, Wafaa Boumya,

Mounia Achak

et al.

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 807, P. 150554 - 150554

Published: Sept. 28, 2021

Language: Английский

Citations

110
Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam DOI Open Access
Đào Nguyên Khôi, Nguyen Trong Quan,

Do Quang Linh

et al.

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1552 - 1552

Published: May 12, 2022

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level existing surface water. This case study aims evaluate performance twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient histogram-based light extreme boosting), three decision tree-based (decision tree, extra trees, random forest), four ANN-based (multilayer perceptron, radial basis function, deep feed-forward neural network, convolutional network), in estimating quality La Buong River Vietnam. Water data at monitoring stations alongside for period 2010–2017 were utilized calculate index (WQI). Prediction ML models was evaluated by using two efficiency statistics (i.e., R2 RMSE). The results indicated that all have good predicting WQI but boosting (XGBoost) has best with highest accuracy (R2 = 0.989 RMSE 0.107). findings strengthen argument especially XGBoost, may be employed prediction a high accuracy, which will further improve management.

Language: Английский

Citations

99