These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
163 related articles for article (PubMed ID: 31018446)
1. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Bui DT; Tsangaratos P; Ngo PT; Pham TD; Pham BT Sci Total Environ; 2019 Jun; 668():1038-1054. PubMed ID: 31018446 [TBL] [Abstract][Full Text] [Related]
2. Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery. Razavi-Termeh SV; Sadeghi-Niaraki A; Seo M; Choi SM Sci Total Environ; 2023 May; 873():162285. PubMed ID: 36801341 [TBL] [Abstract][Full Text] [Related]
3. A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data. Ngo PT; Hoang ND; Pradhan B; Nguyen QK; Tran XT; Nguyen QM; Nguyen VN; Samui P; Tien Bui D Sensors (Basel); 2018 Oct; 18(11):. PubMed ID: 30384451 [TBL] [Abstract][Full Text] [Related]
4. How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region. Saha TK; Pal S; Talukdar S; Debanshi S; Khatun R; Singha P; Mandal I J Environ Manage; 2021 Nov; 297():113344. PubMed ID: 34314957 [TBL] [Abstract][Full Text] [Related]
5. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Arora A; Arabameri A; Pandey M; Siddiqui MA; Shukla UK; Bui DT; Mishra VN; Bhardwaj A Sci Total Environ; 2021 Jan; 750():141565. PubMed ID: 32882492 [TBL] [Abstract][Full Text] [Related]
6. Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Hong H; Liu J; Zhu AX Sci Total Environ; 2020 May; 718():137231. PubMed ID: 32097835 [TBL] [Abstract][Full Text] [Related]
7. A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping. Razavi-Termeh SV; Sadeghi-Niaraki A; Choi SM J Environ Manage; 2023 Nov; 345():118790. PubMed ID: 37647734 [TBL] [Abstract][Full Text] [Related]
8. Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. Wang Y; Hong H; Chen W; Li S; Panahi M; Khosravi K; Shirzadi A; Shahabi H; Panahi S; Costache R J Environ Manage; 2019 Oct; 247():712-729. PubMed ID: 31279803 [TBL] [Abstract][Full Text] [Related]
9. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Hong H; Tsangaratos P; Ilia I; Liu J; Zhu AX; Chen W Sci Total Environ; 2018 Jun; 625():575-588. PubMed ID: 29291572 [TBL] [Abstract][Full Text] [Related]
10. Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. Costache R; Hong H; Pham QB Sci Total Environ; 2020 Apr; 711():134514. PubMed ID: 31812401 [TBL] [Abstract][Full Text] [Related]
11. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Choubin B; Moradi E; Golshan M; Adamowski J; Sajedi-Hosseini F; Mosavi A Sci Total Environ; 2019 Feb; 651(Pt 2):2087-2096. PubMed ID: 30321730 [TBL] [Abstract][Full Text] [Related]
12. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Hong H; Panahi M; Shirzadi A; Ma T; Liu J; Zhu AX; Chen W; Kougias I; Kazakis N Sci Total Environ; 2018 Apr; 621():1124-1141. PubMed ID: 29074239 [TBL] [Abstract][Full Text] [Related]
13. Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Costache R; Tien Bui D Sci Total Environ; 2020 Apr; 712():136492. PubMed ID: 31927448 [TBL] [Abstract][Full Text] [Related]
14. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Razavi Termeh SV; Kornejady A; Pourghasemi HR; Keesstra S Sci Total Environ; 2018 Feb; 615():438-451. PubMed ID: 28988080 [TBL] [Abstract][Full Text] [Related]
15. A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping. Ngo PT; Pham TD; Hoang ND; Tran DA; Amiri M; Le TT; Hoa PV; Bui PV; Nhu VH; Bui DT J Environ Manage; 2021 Feb; 280():111858. PubMed ID: 33360552 [TBL] [Abstract][Full Text] [Related]
16. Integration of hard and soft supervised machine learning for flood susceptibility mapping. Andaryani S; Nourani V; Haghighi AT; Keesstra S J Environ Manage; 2021 Aug; 291():112731. PubMed ID: 33962279 [TBL] [Abstract][Full Text] [Related]
17. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Dodangeh E; Choubin B; Eigdir AN; Nabipour N; Panahi M; Shamshirband S; Mosavi A Sci Total Environ; 2020 Feb; 705():135983. PubMed ID: 31841902 [TBL] [Abstract][Full Text] [Related]
18. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon Khosravi K; Pourghasemi HR; Chapi K; Bahri M Environ Monit Assess; 2016 Dec; 188(12):656. PubMed ID: 27826821 [TBL] [Abstract][Full Text] [Related]
19. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Shafizadeh-Moghadam H; Valavi R; Shahabi H; Chapi K; Shirzadi A J Environ Manage; 2018 Jul; 217():1-11. PubMed ID: 29579536 [TBL] [Abstract][Full Text] [Related]
20. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Tien Bui D; Hoang ND; Martínez-Álvarez F; Ngo PT; Hoa PV; Pham TD; Samui P; Costache R Sci Total Environ; 2020 Jan; 701():134413. PubMed ID: 31706212 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]