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.
125 related articles for article (PubMed ID: 38568312)
1. Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach. Khan NS; Roy SK; Talukdar S; Billah M; Iqbal A; Zzaman RU; Chowdhury A; Mahtab SB; Mallick J Environ Sci Pollut Res Int; 2024 Sep; 31(41):53877-53892. PubMed ID: 38568312 [TBL] [Abstract][Full Text] [Related]
2. Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery. Atchyuth BAS; Swain R; Das P Environ Monit Assess; 2023 Oct; 195(11):1331. PubMed ID: 37848573 [TBL] [Abstract][Full Text] [Related]
3. Basin-wide flood depth and exposure mapping from SAR images and machine learning models. Hao C; Yunus AP; Siva Subramanian S; Avtar R J Environ Manage; 2021 Nov; 297():113367. PubMed ID: 34314958 [TBL] [Abstract][Full Text] [Related]
4. Forecasting of compound ocean-fluvial floods using machine learning. Moradian S; AghaKouchak A; Gharbia S; Broderick C; Olbert AI J Environ Manage; 2024 Jul; 364():121295. PubMed ID: 38875991 [TBL] [Abstract][Full Text] [Related]
5. Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. Tiwari V; Kumar V; Matin MA; Thapa A; Ellenburg WL; Gupta N; Thapa S PLoS One; 2020; 15(8):e0237324. PubMed ID: 32813701 [TBL] [Abstract][Full Text] [Related]
6. Flood mapping and damage assessment due to the super cyclone Yaas using Google Earth Engine in Purba Medinipur, West Bengal, India. Khatun M; Garai S; Sharma J; Singh R; Tiwari S; Rahaman SM Environ Monit Assess; 2022 Oct; 194(12):869. PubMed ID: 36220911 [TBL] [Abstract][Full Text] [Related]
7. Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin. Agnihotri AK; Ohri A; Gaur S; Shivam ; Das N; Mishra S Environ Monit Assess; 2019 Nov; 191(12):760. PubMed ID: 31745827 [TBL] [Abstract][Full Text] [Related]
8. Extreme rainfall-induced urban flood monitoring and damage assessment in Wuhan (China) and Kumamoto (Japan) cities using Google Earth Engine. Pandey AC; Bhattacharjee S; Wasim M; Salim M; Ranjan Parida B Environ Monit Assess; 2022 May; 194(6):402. PubMed ID: 35513557 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models. Youssef AM; Pourghasemi HR; Mahdi AM; Matar SS Environ Sci Pollut Res Int; 2023 Feb; 30(6):16081-16105. PubMed ID: 36178648 [TBL] [Abstract][Full Text] [Related]
11. Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh. Biswas J; Jobaer MA; Haque SF; Islam Shozib MS; Limon ZA Heliyon; 2023 Nov; 9(11):e21245. PubMed ID: 37954389 [TBL] [Abstract][Full Text] [Related]
12. Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt. Youssef AM; Pourghasemi HR; El-Haddad BA Environ Sci Pollut Res Int; 2022 Sep; 29(44):66768-66792. PubMed ID: 35508847 [TBL] [Abstract][Full Text] [Related]
13. Automatic flood detection using sentinel-1 images on the google earth engine. Moharrami M; Javanbakht M; Attarchi S Environ Monit Assess; 2021 Apr; 193(5):248. PubMed ID: 33825990 [TBL] [Abstract][Full Text] [Related]
14. Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data. Cian F; Delgado Blasco JM; Ivanescu C J Environ Manage; 2024 Oct; 369():122326. PubMed ID: 39217900 [TBL] [Abstract][Full Text] [Related]
15. Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment. Singha C; Rana VK; Pham QB; Nguyen DC; Ćupikasza E Environ Sci Pollut Res Int; 2024 Jul; 31(35):48497-48522. PubMed ID: 39030454 [TBL] [Abstract][Full Text] [Related]
16. 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]
17. Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping. Solaimani K; Darvishi S; Shokrian F Environ Sci Pollut Res Int; 2024 May; 31(22):32950-32971. PubMed ID: 38671269 [TBL] [Abstract][Full Text] [Related]
18. 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]
19. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. Rahman M; Chen N; Elbeltagi A; Islam MM; Alam M; Pourghasemi HR; Tao W; Zhang J; Shufeng T; Faiz H; Baig MA; Dewan A J Environ Manage; 2021 Oct; 295():113086. PubMed ID: 34153582 [TBL] [Abstract][Full Text] [Related]
20. Use of machine learning-based classification algorithms in the monitoring of Land Use and Land Cover practices in a hilly terrain. Parashar D; Kumar A; Palni S; Pandey A; Singh A; Singh AP Environ Monit Assess; 2023 Dec; 196(1):8. PubMed ID: 38049547 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]