[HTML][HTML] Applications of machine learning to water resources management: A review of present status and future opportunities

AA Ahmed, S Sayed, A Abdoulhalik, S Moutari… - Journal of Cleaner …, 2024‏ - Elsevier
Water is the most valuable natural resource on earth that plays a critical role in the socio-
economic development of humans worldwide. Water is used for various purposes, including …

[HTML][HTML] A brief review of random forests for water scientists and practitioners and their recent history in water resources

H Tyralis, G Papacharalampous, A Langousis - Water, 2019‏ - mdpi.com
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

Stock closing price prediction using machine learning techniques

M Vijh, D Chandola, VA Tikkiwal, A Kumar - Procedia computer science, 2020‏ - Elsevier
Accurate prediction of stock market returns is a very challenging task due to volatile and non-
linear nature of the financial stock markets. With the introduction of artificial intelligence and …

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

V Rodriguez-Galiano, M Sanchez-Castillo… - Ore geology …, 2015‏ - Elsevier
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …

Data analytics in the supply chain management: Review of machine learning applications in demand forecasting

A Aamer, LP Eka Yani… - Operations and Supply …, 2020‏ - journal.oscm-forum.org
In today's fast-paced global economy coupled with the availability of mobile internet and
social networks, several business models have been disrupted. This disruption brings a …

[HTML][HTML] Renewable energy integration in sustainable water systems: A review

A Zakariazadeh, R Ahshan, R Al Abri… - Cleaner Engineering and …, 2024‏ - Elsevier
The world's demand for water and energy is continuously growing due to population
increase. Traditional water systems are driven by energy produced using fossil fuels, which …

Smart infrastructure: a vision for the role of the civil engineering profession in smart cities

EZ Berglund, JG Monroe, I Ahmed… - Journal of …, 2020‏ - ascelibrary.org
Smart city programs provide a range of technologies that can be applied to solve
infrastructure problems associated with ageing infrastructure and increasing demands. The …

Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an …

V Rodriguez-Galiano, MP Mendes… - Science of the Total …, 2014‏ - Elsevier
Watershed management decisions need robust methods, which allow an accurate predictive
modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data …

[HTML][HTML] A critical review of short-term water demand forecasting tools—what method should i use?

A Niknam, HK Zare, H Hosseininasab, A Mostafaeipour… - Sustainability, 2022‏ - mdpi.com
The challenge for city authorities goes beyond managing growing cities, since as cities
develop, their exposure to climate change effects also increases. In this scenario, urban …

Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

J Sun, L Hu, D Li, K Sun, Z Yang - Journal of Hydrology, 2022‏ - Elsevier
The overexploitation of groundwater resource and its delicacy management has gained
increasing attentions in recent years worldwide because of causing a series of serious …