PRESENT ENVIRONMENT AND SUSTAINABLE DEVELOPMENT

Volume 14, Issue no.1/ 2020

Authors :

Dragomir Andreea ¹,² , Tudorache Andreea-Violeta¹,², Costache Romulus¹,³

1 Faculty of Geography, University of Bucharest, Romania

2 National Institute of Hydrology and Water Management, București, Romania

3 Research Institute of the University of Bucharest, Romania.

E-mail : romuluscostache2000@yahoo.com

https://doi.org/10.15551/pesd2020141010

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1. Bandoc, G., Prăvălie, R. (2015), Climatic water balance dynamics over the last five decades in Romania’s most arid region, Dobrogea, J. Geogr. Sci. 25, 1307-1327. https://doi.org/10.1007/s11442-015-1236-1
2. Bui, D.T., Hoang, N.-D., Martínez-Álvarez, F., Ngo, P.-T.T., Hoa, P.V., Pham, T.D., Samui, P., Costache, R. (2020), A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area, Sci. Total Environ. 701, 134413. https://doi.org/10.1016/j.scitotenv.2019.134413
3. Costache, R. (2014) Using GIS techniques for assessing lag time and concentration time in small river basins. Case study: Pecineaga river basin, Romania. Geo. Tec. 9, 31-38.
4. Costache, R., Fontanine, F., Corodescu E. (2014), Assessment of surface runoff depth changes in Sǎrǎţel River basin, Romania using GIS techniques, Open Geosci. 6, 363-372. https://doi.org/10.2478/s13533-012-0181-0
5. Costache, R. (2019a), Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration, Stoch. Environ. Res. Risk Assess. 33, 1375-1402. https://doi.org/10.1007/s00477-019-01689-9
6. Costache, R. (2019b), Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models-A Useful Tool for Flood Risk Management, Water Resour. Manag. 33, 3239-3256. https://doi.org/10.1007/s11269-019-02301-z
7. Costache, R. (2019c), Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models, Sci. Total Environ. 659, 1115-1134. https://doi.org/10.1016/j.scitotenv.2018.12.397
8. Costache, R., Bui, D.T. (2019), Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania, Sci. Total Environ. 691, 1098-1118. https://doi.org/10.1016/j.scitotenv.2019.07.197
9. Costache, R., Bui, D.T. (2020), Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles,  Sci. Total Environ. 712, 136492. https://doi.org/10.1016/j.scitotenv.2019.136492
10. Costache, R., Hong, H., Pham, Q.B. (2019a), Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models,  Sci. Total Environ. 134514. https://doi.org/10.1016/j.scitotenv.2019.134514
11. Costache, R., Hong, H., Wang, Y. (2019b), Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics, Catena 183, 104179. https://doi.org/10.1016/j.catena.2019.104179
12. Costache, R., Pham, Q.B., Avand, M., Linh, N.T.T., Vojtek, M., Vojteková, J., Lee, S., Khoi, D.N., Nhi, P.T.T., Dung, T.D. (2020a), Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment,  J. Environ. Manage. 265, 110485. https://doi.org/10.1016/j.jenvman.2020.110485
13. Costache, R., Pham, Q.B., Sharifi, E., Linh, N.T.T., Abba, S., Vojtek, M., Vojteková, J., Nhi, P.T.T., Khoi, D.N. (2020b), Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques, Remote Sens. 12, 106. https://doi.org/10.3390/rs12010106
14. Costache, R., Popa, M.C., Bui, D.T., Diaconu, D.C., Ciubotaru, N., Minea, G., Pham, Q.B. (2020c), Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning, J. Hydrol. 124808. https://doi.org/10.1016/j.jhydrol.2020.124808
15. Costache, R., Pravalie, R., Mitof, I., Popescu, C. (2015), Flood vulnerability assessment in the low sector of Saratel Catchment. Case study: Joseni Village, Carpathian J. Earth Environ. Sci. 10, 161-169.
16. Costache, R., Zaharia, L. (2017), Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment - case study: Bâsca Chiojdului River catchment (Romania), J. Earth Syst. Sci. 126, 59. https://doi.org/10.1007/s12040-017-0828-9
17. Fontanine, I., Costache, R. (2013), Using GIS techniques for surface runoff potential analysis in the Subcarpathian area between Buzãu and Slãnic rivers, in Romania, Cinq Cont. 3, 47-57.
18. Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.-X., Chen, W. (2018), Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci. Total Environ. 625, 575-588. https://doi.org/10.1016/j.scitotenv.2017.12.256
19. Khosravi, K., Shahabi, H., Pham, B.T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G., Ho, H.L. (2019), A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods, J. Hydrol. 573, 311-323. https://doi.org/10.1016/j.jhydrol.2019.03.073
20. Mătreață, M., Mătreață, S., Costache, R.-D., Mihalcea, A., Manolache, A.V. (2017), Assessment of Flash Flood Hazard Maps Using Different Threshold Values and Indices Methods, Artic. Submiss. 49. 16), pp. 49-54 (6) http://dx.doi.org/10.5775/fg.2016.059.s
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22. Prăvălie, R., Bandoc, G. (2015), Aridity variability in the last five decades in the Dobrogea region, Romania, Arid Land Res. Manag. 29, 265-287. https://doi.org/10.1080/15324982.2014.977459
23. Prăvălie, R., Bandoc, G., Patriche, C., Sternberg, T. (2019), Recent changes in global drylands: Evidences from two major aridity databases, Catena 178, 209-231. https://doi.org/10.1016/j.catena.2019.03.016
24. Prăvălie, R., Bandoc, G., Patriche, C., Tomescu, M. (2017), Spatio-temporal trends of mean air temperature during 1961–2009 and impacts on crop (maize) yields in the most important agricultural region of Romania. Stoch. Environ,  Res. Risk Assess. 31, 1923-1939. https://doi.org/10.1007/s00477-016-1278-7
25. Smith, G. (2003), Flash flood potential: Determining the hydrologic response of FFMP basins to heavy rain by analyzing their physiographic characteristics. White Pap. Available NWS Colo. Basin River Forecast Cent, Web Site http//www. Cbrfc Noaa Govpapersffpwpap Pdf.
26. Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S., Costache, R. (2019), Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm, J. Env. Manage. 247, 712-729. https://doi.org/10.1016/j.jenvman.2019.06.102
27. Zaharia, L., Minea, G., Ioana-Toroimac, G., Barbu, R., Sârbu, I. (2012), Estimation of the areas with accelerated surface runoff in the upper Prahova watershed (Romanian Carpathians), Balwois, Repub. Maced.