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Articles

Performance of multi-level association rule mining for the relationship between causal factor patterns and flash flood magnitudes in a humid area

ORCID Icon, , &
Pages 1967-1987 | Received 02 Feb 2019, Accepted 01 Aug 2019, Published online: 28 Aug 2019

References

  • Abhishek C, Seshasai MVR, Murthy CS, Rao S. 2012. Assessing early season drought condition using AMSR-E soil moisture product. Geomat Nat Hazards Risk. 4(2):164–184.
  • Agrawal R, Imielinski T, Swami AN. 1993. Mining association rules between sets of items in large database. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. p. 207–216.
  • Agrawal R, Srikant R. 1994. Fast algorithms for mining association rules. Proceeding of 20th VLDB Conference; Santiago, Chile. p. 1–13.
  • Alvarez-Garreton C, Ryu D, Western AW, Su CH, Crow WT, Robertson DE, Leahy C. 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrol Earth Syst Sci. 19(4):1659–1676.
  • Costache R. 2019. 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.
  • 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(4):59.
  • Entekhabi D, Njoku EG, O'Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J, et al. 2010. The soil moisture active passive (SMAP) mission. Proc IEEE. 98(5):704–716.,
  • Gan BR, Liu XN, Yang XG, Wang XK, Zhou JW. 2018. The impact of human activities on the occurrence of mountain flood hazards: lessons from the 17 August 2015 flash flood/debris flow event in Xuyong County, south-western China. Geomat Nat Haz Risk. 9(1):816–840.
  • Gourley JJ, Flamig ZL, Hong Y, Howard KW. 2014. Evaluation of past, present and future tools for radar based flash-flood prediction in the USA. Hrdrol Sci J. 59(7):1377–1389.
  • Gioia A, Manfreda S, Iacobellis V, Fiorentino M. 2014. Performance of a theoretical model for the description of water balance and runoff dynamics in southern Italy. J Hydrol Eng. 19(6):1113–1123.
  • Guo Z, Chi D, Wu J, Zhang WY. 2014. A new wind speed forecasting strategy based on the chaotic time series modeling technique and the Apriori algorithm. Energy Conv Manag. 84:140–151.
  • Grillakis MG, Koutroulis AG, Komma J, Tsanis IK, Wagner W, Bloschl G. 2016. Initial soil moisture effects on flash flood generation – a comparison between basins of contrasting hydro-climatic conditions. J Hydrol. 541:206–217.
  • Koster RD, Mahanama SPP, Livneh B, Lettenmaier DP, Reichle RH. 2010. Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nat Geosci. 3(9):613–616.
  • Li HC, Lei XH, Shang YZ, Qin T. 2018. Flash flood early warning research in China. Int J Water Resour Dev. 34(3):369–385.
  • Liu YY, Dorigo WA, Parinussa RM, de Jeu RAM, Wagner W, McCabe MF, Evans JP, van Dijk AIJM. 2012. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens Environ. 123:280–297.
  • Liu YY, Parinussa RM, Dorigo WA, De Jeu RAM, Wagner W, van Dijk AIJM, McCabe MF, Evans JP. 2011. Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol Earth Syst Sci. 15(2):425–436.
  • Mahmood MI, Elagib NA, Horn F, Saad S. 2017. Lessons learned from Khartoum flash flood impacts: an integrated assessment. Sci Total Environ. 601-602:1031–1045.
  • Manfreda S, Fiorentino M. 2008. A stochastic approach for the description of the water balance dynamics in a river basin. Hydrol Earth Syst Sci Discuss. 5(2):723–748.
  • Manfreda S. 2008. Runoff generation dynamics within a humid river basin. Nat Hazards Earth Syst Sci. 8(6):1349–1357.
  • Massari C, Camici S, Ciabatta L, Brocca L. 2018. Exploiting satellite-based surface soil moisture for flood forecasting in the Mediterranean Area: state update versus rainfall correction. Remote Sens. 10(2):292.
  • Meng SS, Xie XH, Liang SL. 2017. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags. J. Hydrol. 550:568–579.
  • Modrick TM, Georgakakos KP. 2015. The character and causes of flash flood occurrence changes in mountainous small basins of southern California under projected climatic change. J. Hydrol. 3:312–336.
  • Mitchell KE, Lohmann D, Houser PR, Wood EF, Schaake JC, Robock A, Cosgrove BA, Sheffield J, Duan Q, Luo L, et al. 2004. The multi-institution North American Land Data Assimilation System (NLDAS): utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J Geophys Res. 109:D07S90.
  • Matgen P, Fenicia F, Heitz S, Plaza D, de Keyser R, Pauwels VR, Wagner W, Savenije H. 2012. Can ASCAT-derived soil wetness indices reduce predictive uncertainty in well-gauged areas? A comparison with in situ observed soil moisture in an assimilation application. Adv. Water Resour. 44:49–65.
  • Ma J, Tang H, Hu X, Bobet A, Zhang M, Zhu T, Song Y, Eldin M. 2017. Identification of causal factors for the Majiagou landslide using modern data mining methods. Landslides. 14(1):311–322.
  • Meng WG, Wang YQ. 2016. A diagnostic study on heavy rainfall induced by Typhoon Utor (2013) in South China: rainfall asymmetry at landfall. J Geophys Res Atmos. 121:12781–12802.
  • Marco C, Aritz P, Jose AL. 2017. An efficient approximation to the K-means clustering for massive data. Knowledge-Based Syst. 117:56–69.
  • Nahar J, Imam T, Tickle KS, Chen YP. 2013. Association rule mining to detect factors which contribute to heart disease in male and females. Expert Syst Appl. 40(4):1086–1093.
  • Njoku EG. 2004. AMSR-E/Aqua Daily L3 surface soil moisture, interpretive parameters, & QC EASE-grids, version 2. Boulder. Colorado: NASA National Snow and Ice Data Center Distributed Active Archive Center.
  • Pears R, Koh YS, Dobbie G, Yeap W. 2013. Weighted association rule mining via a graph based connectivity model. Inf Sci. 218:61–84.
  • Peng M, Sundararajan V, Williamson T, Minty EP, Smith TC, Doktorchik CTA, Quan H. 2018. Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data. J Biomed Inform. 79:41–47.
  • Qodmanan HR, Nasiri M, Minaei-Bidgoli B. 2011. Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Syst Appl. 38(1):288–298.
  • Renzullo LJ, Van Dijk AI, Perraud JM, Collins D, Henderson B, Jin H, Smith AB, Mcjannet DL. 2014. Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment. J Hydrol. 519:2747–2762.
  • Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, et al. 2004. The global land data assimilation system. Bull Am Meteor Soc. 85(3):381–394.
  • Saharia M, Kirstetter P, Vergara H, Gourley JJ, Hong Y, Giroud M. 2017. Mapping flash flood severity in the United States. J Hydrometeor. 18(2):397–411.
  • Santi E, Paloscia S, Pettinato S, Notarnicola C, Pasolli L, Pistocchi A. 2013. Comparison between SAR soil moisture estimates and hydrological model simulations over the Scrivia Test Site. Remote Sens. 5(10):4961–4976.
  • Scipal K, Scheffler C, Wagner W. 2005. Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing. Hydrol Earth Syst Sci. 9(3):173–183.
  • Shen CP. 2018. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour Res. 54(11):8558–8593.
  • Son LH, Chiclana F, Kumar R, Mittal M, Khar M, Chatterjee JM, Baik SW. 2018. ARM-AMO: An efficient association rule mining algorithm based on animal migration optimization. Knowledge-Based Syst. 154:68–80.
  • Sina K, Naiier A, Samaneh S. 2017. An improved overlapping k-means clustering method for medical applications. Expert Syst Appl. 67:12–18.
  • Wagner W, Dorigo W, de Jeu R, Fernandez D, Benveniste J, Haas E, Ertl M. 2012. Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I–7, XXII ISPRS Congress; August 25–September 1; Melbourne, Australia. p. 315–321.
  • Wu GL, Yang Z, Cui Z, Liu Y, Fang NF, Shi ZH. 2016. Mixed artificial grasslands with more roots improved mine soil infiltration capacity. J Hydrol. 535:54–60.
  • Youssef AM, Sefry SA, Pradhan B, Alfadail EA. 2016. Analysis on causes of flash flood in Jeddah city (Kingdom of Saudi Arabia) of 2009 and 2011 using multi-sensor remote sensing data and GIS. Geomat Nat Haz Risk. 7(3):1018–1042.
  • Zaharia L, Costache R, Prăvălie R, Ioana-Toroimac G. 2017. Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania). Front Earth Sci. 11(2):229–247.
  • Zhai X, Guo L, Liu R, Zhang Y. 2018. Rainfall threshold determination for flash flood warning in mountainous catchments with consideration of antecedent soil moisture and rainfall pattern. Nat Hazards. 94(2):605–625.
  • Zhang Q, Jiang T, Chen YQD, Chen XH. 2010. Changing properties of hydrological extremes in south China: natural variations or human influences? Hydrol Process. 24(11):1421–1432.
  • Zheng YG, Xue M, Li B, Chen J, Tao ZY. 2016. Spatial characteristics of extreme rainfall over China with hourly through 24-hour accumulation periods based on national-level hourly rain gauge data. Adv Atmos Sci. 33(11):1218–1232.