Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping DOI Open Access
Romulus Costache, Phuong Thao Thi Ngo, Dieu Tien Bui

et al.

Water, Journal Year: 2020, Volume and Issue: 12(6), P. 1549 - 1549

Published: May 29, 2020

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In regard, geospatial database the flood with 178 locations 10 predictors prepared used AHP FR were processing coding into numeric format, whereas DNN, which is powerful state-of-the-art probabilistic machine leaning, employed build an inference model. The reliability models verified help Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), several statistical measures. result shows that two ensemble models, DNN-AHP DNN-FR, are capable predicting future areas accuracy higher than 92%; therefore, they tool studies.

Language: Английский

Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms DOI
Anurag Malik, Yazid Tikhamarine,

Saad Sh. Sammen

et al.

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 28(29), P. 39139 - 39158

Published: March 22, 2021

Language: Английский

Citations

99
A review on flood management technologies related to image processing and machine learning DOI
Hafiz Suliman Munawar, Ahmed W. A. Hammad, S. Travis Waller

et al.

Automation in Construction, Journal Year: 2021, Volume and Issue: 132, P. 103916 - 103916

Published: Sept. 9, 2021

Language: Английский

Citations

94
Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS: a case study of Memari Municipality, India DOI
Sk Ajim Ali, Farhana Parvin, Nadhir Al‐Ansari

et al.

Environmental Science and Pollution Research, Journal Year: 2020, Volume and Issue: 28(6), P. 7528 - 7550

Published: Oct. 9, 2020

Language: Английский

Citations

87
Threats of climate change and land use patterns enhance the susceptibility of future floods in India DOI
Subodh Chandra Pal, Indrajit Chowdhuri, Biswajit Das

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 305, P. 114317 - 114317

Published: Dec. 24, 2021

Language: Английский

Citations

76
Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping DOI Open Access
Romulus Costache, Phuong Thao Thi Ngo, Dieu Tien Bui

et al.

Water, Journal Year: 2020, Volume and Issue: 12(6), P. 1549 - 1549

Published: May 29, 2020

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In regard, geospatial database the flood with 178 locations 10 predictors prepared used AHP FR were processing coding into numeric format, whereas DNN, which is powerful state-of-the-art probabilistic machine leaning, employed build an inference model. The reliability models verified help Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), several statistical measures. result shows that two ensemble models, DNN-AHP DNN-FR, are capable predicting future areas accuracy higher than 92%; therefore, they tool studies.

Language: Английский

Citations

71