Abstract
Uncertainty analysis have attracted increasing attention of both theory and application over the last decades. Owing to the complex of surrounding, uncertainty analysis of rainfall in urban area is very little. Existing literatures on uncertainty analysis paid less attention on gauge density and rainfall intensity. Therefore, this study focuses on urban area, which a good complement to uncertainty research. In this study, gauge density was investigated with carefully selecting of gauge to covering evenly. Rainfall intensity data were extracted from one rainfall event at begin, summit and ending phases of rainfall process. Three traditional methods (Ordinary Kriging, RBF and IDW) and three machine methods (RF, ANN and SVM) were investigated for the uncertainty analysis. The result shows that (1) gauge density has important influence on the interpolation accuracy, and the higher gauge density means the higher accuracy. (2) The uncertainty is progressively stable with the increasing of rainfall intensity. (3) Geostatistic methods has better result than the IDW and RBF owing to considering spatial variability. The selected machine learning methods have good performance than traditional methods. However, the complex training processing and without spatial variability may reduce its practicability in modern flood management. Therefore, the combining of traditional methods and machine learning will be the good paradigm for spatial interpolation and uncertainty analysis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bárdossy, A., Pegram, G.: Interpolation of precipitation under topographic influence at different time scales. Water Resour. Res. 49(8), 4545–4565 (2013)
Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J. Hydrol. 228(1–2), 113–129 (2000)
Jeffrey, S.J., Carter, J.O., Moodie, K.B., Beswick, A.R.: Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model Softw. 16(4), 309–330 (2001)
Li, J., Heap, A.D.: Spatial interpolation methods applied in the environmental sciences: a review. Environ. Model Softw. 53, 173–189 (2014)
Muthusamy, M., Schellart, A., Tait, S., Heuvelink, G.B.M.: Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models. Hydrol. Earth Syst. Sci. 21(2), 1077–1091 (2017)
Wagner, P.D., Fiener, P., Wilken, F., Kumar, S., Schneider, K.: Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J. Hydrol. 464–465, 388–400 (2012)
Courty, L., Rico-Ramirez, M., Pedrozo-Acuña, A.: The significance of the spatial variability of rainfall on the numerical simulation of urban floods. Water 10(2), 207 (2018)
Hall, J., Solomatine, D.: A framework for uncertainty analysis in flood risk management decisions. Int. J. River Basin Manag. 6(2), 85–98 (2008)
Hutter, G., Schanze, J.: Learning how to deal with uncertainty of flood risk in long-term planning. Int. J. River Basin Manag. 6(2), 175–184 (2008)
Hrachowitz, M., Weiler, M.: Uncertainty of precipitation estimates caused by sparse gauging networks in a small. Mountainous Watershed. J. Hydrol. Eng. 16(5), 460–471 (2011)
Tsintikidis, D., Georgakakos, K.R., Sperfslage, J.A., Smith, D.E., Carpenter, T.M.: Precipitation uncertainty and raingauge network design within Folsom Lake watershed. J. Hydrol. Eng. 7(2), 175–184 (2002)
Cheng, M., et al.: Performance assessment of spatial interpolation of precipitation for hydrological process simulation in the Three Gorges Basin. Water 9(11), 838 (2017)
Rupa, C., Mujumdar, P.P.: Quantification of uncertainty in spatial return levels of urban precipitation extremes. J. Hydrol. Eng. 23(1), 04017053(2018)
Yang, L., Tian, F., Niyogi, D.: A need to revisit hydrologic responses to urbanization by incorporating the feedback on spatial rainfall patterns. Urban Clim. 12, 128–140 (2015)
Liu, M., Bárdossy, A., Zehe, E.: Interaction of valleys and circulation patterns (CPs) on spatial precipitation patterns in southern Germany. Hydrol. Earth Syst. Sci. 17(11), 4685–4699 (2013)
Otieno, H., Yang, J., Liu, W., Han, D.: Influence of rain gauge density on interpolation method selection. J. Hydrol. Eng. 19(11), 04014024(2014)
Jing, C., Yu, J., Dai, P., Wei, H., Du, M.: Rule-based rain gauge network design in urban areas aided by spatial kernel density. Water Pract. Technol. 11(1), 166–175 (2016)
Moulin, L., Gaume, E., Obled, C.: Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations. Hydrol. Earth Syst. Sci. 13(2), 99–114 (2009)
Kobold, M., Sušelj, K.: Precipitation forecasts and their uncertainty as input into hydrological models. Hydrol. Earth Syst. Sci. 9(4), 322–332 (2005)
Ly, S., Charles, C., Degré, A.: Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review. Base 17(2), 392–406 (2013)
Li, J., Heap, A.D.: A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol. Inform. 6(3–4), 228–241 (2011)
de Amorim Borges, P., Franke, J., da Anunciação, Y.M.T., Weiss, H., Bernhofer, C.: Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil. Theor. Appl. Climatol. 123(1–2), 335–348 (2016)
Appelhans, T., Mwangomo, E., Hardy, D.R., Hemp, A., Nauss, T.: Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania. Spat. Stat. 14, 91–113 (2015)
Gilardi, S., Begio, N.: Local machine learning models for spatial data analysis. J. Geogr. Inf. Decis. Anal. 4(EPFL-ARTICLE-82651), 11–28 (2000)
Li, J., Heap, A.D., Potter, A., Daniell, J.J.: Application of machine learning methods to spatial interpolation of environmental variables. Environ. Model Softw. 26(12), 1647–1659 (2011)
Hengl, T., Heuvelink, G.B.M., Rossiter, D.G.: About regression-Kriging: from equations to case studies. Comput. Geosci. 33(10), 1301–1315 (2007)
Bhargava, N., Bhargava, R., Tanwar, P.S., Narooka, P.C.: Comparative study of inverse power of IDW interpolation method in inherent error analysis of aspect variable. In: Mishra, D., Nayak, M., Joshi, A. (eds.) Information and Communication Technology for Sustainable Development, pp. 521–529. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3920-1_52
Maciej, T.: Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW) - cross-validation/Jackknife approach. J. Geogr. Inf. Decis. Anal. 2(2), 18–30 (1998)
Adhikary, S.K., Muttil, N., Yilmaz, A.G.: Genetic programming-based Ordinary Kriging for spatial interpolation of rainfall. J. Hydrol. Eng. 21(2), 1–14 (2016)
Berndt, C., Rabiei, E., Haberlandt, U.: Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. J. Hydrol. 508, 88–101 (2014)
ESRI: How radial basis functions work (2013)
Xie, Y., et al.: Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere 82(3), 468–476 (2011)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Genuer, R., Poggi, J.M., Tuleau-Malot, C., Villa-Vialaneix, N.: Random forests for big data. Big Data Res. 9, 28–46 (2017)
Kühnlein, M., Appelhans, T., Thies, B., Nauss, T.: Improving the accuracy of rainfall rates from optical satellite sensors with machine learning - a random forests-based approach applied to MSG SEVIRI. Remote Sens. Environ. 141, 129–143 (2014)
Basheer, I.A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000)
Prasad, R., Deo, R.C., Li, Y., Maraseni, T.: Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm. Atmos. Res. 197, 42–63 (2017)
Cortes, C., Cortes, C., Vapnik, V., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
Kavzoglu, T., Colkesen, I.: A kernel functions analysis for support vector machines for land cover classification. Int. J. Appl. Earth Obs. Geoinf. 11(5), 352–359 (2009)
Sadler, J.M., Goodall, J.L., Morsy, M.M.: Effect of rain gauge proximity on rainfall estimation for problematic urban coastal watersheds in Virginia Beach, Virginia. J. Hydrol. Eng. 22(9), 04017036(2017)
Cox, J.C., Sadiraj, V.: On the coefficient of variation as a measure of risk sensitivity. SSRN 3(3), (2011)
Reed, G.F., Lynn, F., Meade, B.D.: Quantitative assays. Clin. Diagn. Lab. Immunol. 9(6), 1235–1239 (2002)
Cristiano, E., Veldhuis, M.-C., Van De Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas-a review. Hydrol. Earth Syst. Sci. 21, 3859–3878 (2017)
WMO: Guide to Meteorological Instruments and Methods of observation (WMO-No.8), Seven edit. Geneva, Switzerland (2008)
Goovaerts, P.: Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89(1–2), 1–45 (1999)
Ma, L., Chi, X., Zuo, C.: Evaluation of interpolation models for rainfall erosivity on a large scale. In: First International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5. IEEE, Shanghai (2012)
Zhang, P., Liu, R., Bao, Y., Wang, J., Yu, W., Shen, Z.: Uncertainty of SWAT model at different DEM resolutions in a large mountainous watershed. Water Res. 53, 132–144 (2014)
Acknowledgments
The authors would like to thank the valuable comments from anonymous reviewers. This study is jointly supported by the National Natural Science Foundation of China (Grant No. 41771412), the Beijing Natural Science Foundation (Grant No. 8182015), Beijing Advanced innovation center for future urban design (Grant No. X18052, X18058, X18158) and the Zhejiang Province Research Program (Grant No. 2015C33064).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Huang, J., Jing, C., Fu, J., Huang, Z. (2019). Uncertainty Analysis of Rainfall Spatial Interpolation in Urban Small Area. In: Gao, H., Yin, Y., Yang, X., Miao, H. (eds) Testbeds and Research Infrastructures for the Development of Networks and Communities. TridentCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-030-12971-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-12971-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12970-5
Online ISBN: 978-3-030-12971-2
eBook Packages: Computer ScienceComputer Science (R0)