Differentiable modelling to unify machine learning and physical models for geosciences

C Shen, AP Appling, P Gentine, T Bandai… - Nature Reviews Earth & …, 2023 - nature.com
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

A survey on river water quality modelling using artificial intelligence models: 2000–2020

TM Tung, ZM Yaseen - Journal of Hydrology, 2020 - Elsevier
There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …

Application of machine learning in intelligent fish aquaculture: A review

S Zhao, S Zhang, J Liu, H Wang, J Zhu, D Li, R Zhao - Aquaculture, 2021 - Elsevier
Among the background of developments in automation and intelligence, machine learning
technology has been extensively applied in aquaculture in recent years, providing a new …

Boosting principles for the photocatalytic performance of Cr-doped Cu2O crystallites and mechanisms of photocatalytic oxidation for levofloxacin

J Nie, X Yu, Z Liu, Y Wei, J Zhang, N Zhao, Z Yu… - Applied Surface …, 2022 - Elsevier
Cr-doped Cu 2 O crystallites were synthesized by hydrothermal process. XRD, BET, SEM,
TEM, UV–Vis and RSM were used to reveal the microstructure, optical characteristics and …

Water quality prediction using SWAT-ANN coupled approach

N Noori, L Kalin, S Isik - Journal of Hydrology, 2020 - Elsevier
Efficient and accurate prediction of river water quality is challenging due to the complex
hydrological and environmental processes affecting their nature. The challenge is even …

[HTML][HTML] Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends

H Rastegari, F Nadi, SS Lam, M Ikhwanuddin… - Smart Agricultural …, 2023 - Elsevier
Aquaculture produces nearly half of the seafood consumed by the ever-growing world
population. There have been attempts to adopt novel technologies into the industry to …

[HTML][HTML] Overview of smart aquaculture system: Focusing on applications of machine learning and computer vision

TTE Vo, H Ko, JH Huh, Y Kim - Electronics, 2021 - mdpi.com
Smart aquaculture is nowadays one of the sustainable development trends for the
aquaculture industry in intelligence and automation. Modern intelligent technologies have …

Hybrid machine learning models for prediction of daily dissolved oxygen

A Azma, Y Liu, M Azma, M Saadat, D Zhang… - Journal of Water …, 2023 - Elsevier
Measuring water quality parameters is a significant step in many hydrological assessments.
Dissolved oxygen (DO) is one of these parameters that is an indicator of water quality …

Predicting load capacity of shear walls using SVR–RSM model

B Keshtegar, ML Nehdi, NT Trung, R Kolahchi - Applied Soft Computing, 2021 - Elsevier
Accurate prediction of the shear capacity of reinforced concrete shear walls (RCSW) is
essential for the wind and seismic design of buildings. However, due to the diverse structural …

Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree

B Keshtegar, C Mert, O Kisi - Renewable and sustainable energy reviews, 2018 - Elsevier
In this study, four different heuristic regression methods including Kriging, response surface
method (RSM), multivariate adaptive regression (MARS) and M5 model tree (M5Tree) have …