Machine learning in geo-and environmental sciences: From small to large scale

P Tahmasebi, S Kamrava, T Bai, M Sahimi - Advances in Water Resources, 2020‏ - Elsevier
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

An overview of energy demand forecasting methods published in 2005–2015

I Ghalehkhondabi, E Ardjmand, GR Weckman… - Energy Systems, 2017‏ - Springer
The importance of energy demand management has been more vital in recent decades as
the resources are getting less, emission is getting more and developments in applying …

Water quality modelling using principal component analysis and artificial neural network

A Ibrahim, A Ismail, H Juahir, AB Iliyasu… - Marine Pollution …, 2023‏ - Elsevier
The study investigates the latent pollution sources and most significant parameters that
cause spatial variation and develops the best input for water quality modelling using …

Deterministic and probabilistic health risk assessment techniques to evaluate non-carcinogenic human health risk (NHHR) due to fluoride and nitrate in groundwater …

L Kaur, MS Rishi, AU Siddiqui - Environmental Pollution, 2020‏ - Elsevier
Human interferences have caused groundwater contamination in alluvial aquifers which
subsequently affects the health of exposed population. In the present study, 74 groundwater …

A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2. 5

R Zhao, L Zhan, M Yao, L Yang - Sustainable Cities and Society, 2020‏ - Elsevier
This study develops an augmented geographically weighted regression (GWR) model to
analyze the spatial distribution of PM 2.5 concentrations through the incorporation of …

Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan

S Shrestha, F Kazama - Environmental modelling & software, 2007‏ - Elsevier
Multivariate statistical techniques, such as cluster analysis (CA), principal component
analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the …

Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

SIV Sousa, FG Martins, MCM Alvim-Ferraz… - … Modelling & Software, 2007‏ - Elsevier
The prediction of tropospheric ozone concentrations is very important due to the negative
impacts of ozone on human health, climate and vegetation. The development of models to …

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

D Dominick, H Juahir, MT Latif, SM Zain… - Atmospheric environment, 2012‏ - Elsevier
This study aims to investigate possible sources of air pollutants and the spatial patterns
within the eight selected Malaysian air monitoring stations based on a two-year database …

Ozone modelling and map** for risk assessment: an overview of different approaches for human and ecosystems health

A De Marco, H Garcia-Gomez, A Collalti… - Environmental …, 2022‏ - Elsevier
Tropospheric ozone (O 3) is one of the most concernedair pollutants dueto its widespread
impacts on land vegetated ecosystems and human health. Ozone is also the third …

Industry 4.0 and demand forecasting of the energy supply chain: A literature review

AR Nia, A Awasthi, N Bhuiyan - Computers & Industrial Engineering, 2021‏ - Elsevier
The number of publications in demand forecasting of the energy supply chain augmented
meaningfully due to the 2008 global financial crisis and its consequence on the global …