A brief review of random forests for water scientists and practitioners and their recent history in water resources
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …
to gain prominence in water resources applications. However, existing applications are …
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 …
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
Stock closing price prediction using machine learning techniques
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 …
linear nature of the financial stock markets. With the introduction of artificial intelligence and …
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 …
social networks, several business models have been disrupted. This disruption brings a …
Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an …
Watershed management decisions need robust methods, which allow an accurate predictive
modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data …
modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data …
Understanding smart city—a data-driven literature review
This paper systematically reviews the top 200 Google Scholar publications in the area of
smart city with the aid of data-driven methods from the fields natural language processing …
smart city with the aid of data-driven methods from the fields natural language processing …
Urban water demand forecasting: review of methods and models
This paper reviews the literature on urban water demand forecasting published from 2000 to
2010 to identify methods and models useful for specific water utility decision making …
2010 to identify methods and models useful for specific water utility decision making …
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 …
increasing attentions in recent years worldwide because of causing a series of serious …
Urban water demand modeling: Review of concepts, methods, and organizing principles
In this paper, we use a theoretical framework of coupled human and natural systems to
review the methodological advances in urban water demand modeling over the past 3 …
review the methodological advances in urban water demand modeling over the past 3 …
Variable selection in time series forecasting using random forests
Time series forecasting using machine learning algorithms has gained popularity recently.
Random forest is a machine learning algorithm implemented in time series forecasting; …
Random forest is a machine learning algorithm implemented in time series forecasting; …