Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana

References

• Addai, M. O., Yidana, S. M., Chegbeleh, L. P., Adomako, D., & Banoeng-Yakubo, B. (2016). Groundwater recharge processes in the nasia sub-catchment of the white volta basin: analysis of porewater characteristics in the unsaturated zone. Journal of African Earth Sciences, 122, 4–16. https://doi.org/10.1016/j.jafrearsci.2015.04.006
   Web of Science ®Google Scholar
• Armah, F. A., Yawson, D. O., Yengoh, G. T., Odoi, J. O., & Afrifa, E. K. (2010). Impact of floods on livelihoods and vulnerability of natural resource dependent communities in Northern Ghana. Water, 2(2), 120–139. https://doi.org/10.3390/w2020120
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Cian, F., Marconcini, M., & Ceccato, P. (2018). Normalized difference flood index for rapid flood mapping: taking advantage of EO big data. Remote Sensing of Environment, 209, 712–730. https://doi.org/10.1016/j.rse.2018.03.006
   Web of Science ®Google Scholar
• Du, J., Fang, J., Xu, W., & Shi, P. (2013). Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province China. Stochastic Environmental Research and Risk Assessment : Research Journal, 27(2), 377–387. https://doi.org/10.1007/s00477-012-0589-6
   Web of Science ®Google Scholar
• Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
   Web of Science ®Google Scholar
• Forkuo, E. K. (2011). Flood hazard mapping using aster image data with GIS. International Journal of Geomatics and Geosciences, 1(4), 932-950. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.422.269&rep=rep1&type=pdf
   Google Scholar
• Forkuor, G., Hounkpatin, O. K., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: A comparison of machine learning and multiple linear regression models. PloS One, 12(1), e0170478. https://doi.org/10.1371/journal.pone.0170478
   PubMed Web of Science ®Google Scholar
• Gautam, V. K., Gaurav, P. K., Murugan, P., & Annadurai, M. (2015). Assessment of surface water dynamicsin Bangalore using WRI, NDWI, MNDWI, supervised classification and KT transformation. Aquatic Procedia, 4, 739–746. https://doi.org/10.1016/j.aqpro.2015.02.095
   Google Scholar
• Ghoneim, E., & Foody, G. M. (2013). Assessing flash flood hazard in an arid mountainous region. Arab. J. Geosci, 6 (4), 1191–1202. [CrossRef]. https://doi.org/10.1007/s12517-011-0411-7
   Web of Science ®Google Scholar
• GOG Offical Portal (Government of Ghana). (2019). Ghana.gov.gh
   Google Scholar
• Goldberg, M. D., Kilcoyne, H., Cikanek, H., & Mehta, A. (2013). Joint polar satellite system: the United States next generation civilian polar‐orbiting environmental satellite system. Journal of Geophysical Research: Atmospheres, 118(24), 13–463. https://doi.org/10.1002/2013JD020389
   Web of Science ®Google Scholar
• Ho, T. K. (1995). Random decision forests. In #60;italic#62;Proceedings of 3rd international conference on document analysis and recognition, Montreal, Canada#60;/italic#62; (Vol. 1, pp. 278-282). IEEE. https://doi.org/10.5555/844379.844681
   Google Scholar
• IPCC. (2007). Climate Change. Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change.
   Google Scholar
• Janitza, S., & Hornung, R. (2018). On the overestimation of random forest’s out-of-bag error. PloS One, 13(8), p.e0201904. https://doi.org/10.1371/journal.pone.0201904
   Google Scholar
• Jonathan, D. W., Jennifer, L. I., & Suzana, J. C. (2013). Coastal flooding by tropical cyclones and sea-level rise. Nature, 504(7478), 44–52. https://doi.org/10.1038/nature12855
   PubMed Web of Science ®Google Scholar
• McFEETERS, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
   Web of Science ®Google Scholar
• Memon, A. A., Muhammad, S., Rahman, S., & Haq, M. (2015). Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 99–106. https://doi.org/10.1016/j.ejrs.2015.03.003
   Web of Science ®Google Scholar
• Messner, F., & Meyer, V. (2006). Flood damage, vulnerability and risk perception–challenges for flood damage research. In Schanze J., Zeman E., Marsalek J. (Eds.), Flood Risk Management: Hazards, Vulnerability and Mitigation Measures (pp. 149–167). Springer.
   Google Scholar
• Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), .87–106. https://doi.org/10.1257/jep.31.2.87
   Google Scholar
• Nicholls, R., Burcharth, H. F., Zanuttigh, B., Andersen, T. L., Orcid, L., Lara, J. L., Steendam, G. J., Roul, P., Sergent, P., & Ostrowski, R. (2015). Developing a holistic approach to assessing and managing coastal flood risk. In Coastal Risk Management in a Changing Climate (pp. 9–53). Elsevier—Butterworth-Heinemann.
   Google Scholar
• Nsor, C. A., Ashiagbor, G., & Danquah, E. (2019). Quantifying recent floodplain vegetation change along the white Volta river in the Northern region of Ghana. Ghana Journal of Geography, 11(1), 150–170. https://www.ajol.info/index.php/gjg/article/view/186827
   Google Scholar
• Pan, F., Xi, X., & Wang, C. (2020). A comparative study of water indices and image classification algorithms for mapping inland surface water bodies using landsat imagery. Remote Sensing, 12(10), 1611. https://doi.org/10.3390/rs12101611
   Web of Science ®Google Scholar
• Probst, P., & Boulesteix, A. L. (2018). To tune or not to tune the number of trees in random forest. Journal of Machine Learning Research, 18(181), 1–18. https://www.jmlr.org/papers/volume18/17-269/17-269.pdf
   Google Scholar
• Ramin, B. M., & McMichael, A. J. (2009 March). Climate change and health in sub-Saharan Africa: A case-based perspective. Ecohealth, 6(1), 52–57. https://doi.org/10.1007/s10393-009-0222-4. Epub 2009 May 6. PMID: 19418098.
   PubMed Web of Science ®Google Scholar
• Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., & Scambos, T. A. (2014). Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
   Web of Science ®Google Scholar
• Schumann, G. J.-P., Brakenridge, G. R., Kettner, A. J., Kashif, R., & Niebuhr, E. (2018). Assisting flood disaster response with earth observation data and products: A critical assessment. Remote Sens, 10(8), 1230. https://doi.org/10.3390/rs10081230
   Google Scholar
• Shabani, F., Kumar, L., & Esmaeili, A. (2014). Improvement to the prediction of the USLE K factor. Geomorphology, 204, 229–234. https://doi.org/10.1016/j.geomorph.2013.08.008
   Web of Science ®Google Scholar
• Singh, O., & Kumar, M. (2013). Flood events, fatalities and damages in India from 1978 to 2006. Nat Hazards, 69(3), 1815–1834. https://doi.org/10.1007/s11069-013-0781-0
   Web of Science ®Google Scholar
• Stijn, T., Patrick, M., Tjeerd, J. B., Peter, M. J. H., Ysebaert, T., & Vriend,, H. J. D. (2013). Ecosystem-based coastal defence in the face of global change. Nature, 504(7478), 79–83. https://doi.org/10.1038/nature12859
   PubMed Web of Science ®Google Scholar
• Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., & Dwyer, J. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147.https://doi.org/10.1016/j.rse.2019.02.015#x00A0;
   Web of Science ®Google Scholar
• Xu, C., Chen, Y., Chen, Y., Zhao, R., & Ding, H. (.2013). Responses of surface runoff to climate change and human activities in the arid region of Central Asia: A case study in the Tarim River Basin, China. Environ Manag, 51(4), 926–938. https://doi.org/10.1007/s00267-013-0018-8
   PubMed Web of Science ®Google Scholar
• Yang, H., Wang, Z., Zhao, H., & Guo, Y. (2011). Water body extraction methods study based on RS and GIS. Procedia Environmental Sciences, 10(PARTC)), 2619–2624. https://doi.org/10.1016/j.proenv.2011.09.407
   Google Scholar
• Yiran, G. A. and Stringer, L. C. (2016). Spatio-temporal analyses of impacts of multiple climatic hazards in a savannah ecosystem of Ghana. Climate Risk Management, 14, 11–26
   Web of Science ®Google Scholar