Weather impact on solar farm performance: a comparative analysis of machine learning techniques

A Gopi, P Sharma, K Sudhakar, WK Ngui… - Sustainability, 2022 - mdpi.com
Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for
establishing the economic sustainability of a newly installed system. The present study aims …

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

AL Collins, M Blackwell, P Boeckx, CA Chivers… - Journal of Soils and …, 2020 - Springer
Purpose This review of sediment source fingerprinting assesses the current state-of-the-art,
remaining challenges and emerging themes. It combines inputs from international scientists …

Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin

Z Xu, P Belmont, J Brahney, AC Gellis - Journal of Environmental …, 2022 - Elsevier
Reliable quantitative information on sediment sources to rivers is critical to mitigate
contamination and target conservation and restoration actions. However, for large-scale …

Comparative evaluation of AI‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4‐coated MWCNT hybrid nanofluids for …

P Sharma, Z Said, S Memon… - … Journal of Energy …, 2022 - Wiley Online Library
Hybrid nanofluids are gaining popularity owing to the synergistic effects of nanoparticles,
which provide them with better heat transfer capabilities than base fluids and normal …

Precise prediction of performance and emission of a waste derived Biogas–Biodiesel powered Dual–Fuel engine using modern ensemble Boosted regression Tree: A …

P Sharma, BB Sahoo - Fuel, 2022 - Elsevier
The current study looks at using waste-derived biodiesel as pilot fuel and waste-derived
biogas as a gaseous fuel to power a diesel engine in dual-fuel mode. A new ensemble …

Next-generation remote sensing and prediction of sand and dust storms: State-of-the-art and future trends

P Jiao, J Wang, X Chen, J Ruan, X Ye… - International Journal of …, 2021 - Taylor & Francis
Sand and dust storms (SDS) have long been considered as a type of disastrous weather.
Minimizing the severe influence of SDS on the environment and human life has been the …

Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach

JD German, AKS Ong, AANP Redi, KPE Robas - Heliyon, 2022 - cell.com
The COVID-19 pandemic had brought changes to individuals, especially in consumer
behavior. As the government of different countries has been implementing safety protocols …

A machine learning ensemble approach for predicting factors affecting STEM students' future intention to enroll in chemistry-related courses

AKS Ong - Sustainability, 2022 - mdpi.com
The need for chemistry-related professionals has been evident with the rise of global issues
such as the pandemic and global warming. Studies have indicated how an increase in the …

[HTML][HTML] Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques

Z Ebrahimi-Khusfi, AR Nafarzadegan, F Dargahian - Ecological Indicators, 2021 - Elsevier
In the past decades, some desert wetlands have become critical regions for dust production
in the arid and semi-arid regions of the world. Accurate prediction of the number of dusty …

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

H Gholami, A Mohammadifar - Scientific Reports, 2022 - nature.com
Dust storms have many negative consequences, and affect all kinds of ecosystems, as well
as climate and weather conditions. Therefore, classification of dust storm sources into …