Designing novel superwetting surfaces for high-efficiency oil–water separation: design principles, opportunities, trends and challenges

L Qiu, Y Sun, Z Guo - Journal of Materials Chemistry A, 2020 - pubs.rsc.org
Membrane filtration and absorption strategies based on superwetting surfaces for oil–water
separation have regained tremendous attention due to their being low cost, highly efficient …

Random forest in remote sensing: A review of applications and future directions

M Belgiu, L Drăguţ - ISPRS journal of photogrammetry and remote sensing, 2016 - Elsevier
A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier …

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic …

D Tien Bui, TA Tuan, H Klempe, B Pradhan, I Revhaug - Landslides, 2016 - Springer
Preparation of landslide susceptibility maps is considered as the first important step in
landslide risk assessments, but these maps are accepted as an end product that can be …

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

JN Goetz, A Brenning, H Petschko, P Leopold - Computers & geosciences, 2015 - Elsevier
Statistical and now machine learning prediction methods have been gaining popularity in
the field of landslide susceptibility modeling. Particularly, these data driven approaches …

Sensors, features, and machine learning for oil spill detection and monitoring: A review

R Al-Ruzouq, MBA Gibril, A Shanableh, A Kais… - Remote Sensing, 2020 - mdpi.com
Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …

[HTML][HTML] UAV remote sensing for urban vegetation mapping using random forest and texture analysis

Q Feng, J Liu, J Gong - Remote sensing, 2015 - mdpi.com
Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping
in complex urban landscapes due to the ultra-high resolution imagery acquired at low …

A comparative study of machine learning classifiers for modeling travel mode choice

J Hagenauer, M Helbich - Expert Systems with Applications, 2017 - Elsevier
The analysis of travel mode choice is an important task in transportation planning and policy
making in order to understand and predict travel demands. While advances in machine …

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

H Hong, B Pradhan, C Xu, DT Bui - Catena, 2015 - Elsevier
Preparation of landslide susceptibility map is the first step for landslide hazard mitigation
and risk assessment. The main aim of this study is to explore potential applications of two …

Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—A case of Yuyao, China

Q Feng, J Liu, J Gong - Water, 2015 - mdpi.com
Flooding is a severe natural hazard, which poses a great threat to human life and property,
especially in densely-populated urban areas. As one of the fastest developing fields in …

Application of machine learning in ocean data

R Lou, Z Lv, S Dang, T Su, X Li - Multimedia Systems, 2023 - Springer
In recent years, machine learning has become a hot research method in various fields and
has been applied to every aspect of our life, providing an intelligent solution to problems that …