291
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

& ORCID Icon
Pages 554-571 | Received 11 Jul 2022, Accepted 07 Jan 2023, Published online: 16 Mar 2023

References

  • Abuzied, S., and B. Pradhan. 2021. “Hydro-geomorphic Assessment of Erosion Intensity and Sediment Yield Initiated Debris-Flow Hazards at Wadi Dahab Watershed, Egypt.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 15 (3): 221–246. doi:10.1080/17499518.2020.1753781.
  • Andrieu, C., N. De Freitas, A. Doucet, and M. Jordan. 2003. “An Introduction to MCMC for Machine Learning.” Machine Learning 50: 5–43. doi:10.1023/A:1020281327116.
  • Arnaud, P., C. Bouvier, L. Cisneros, and R. Dominguez. 2002. “Influence of Rainfall Spatial Variability on Flood Prediction.” Journal of Hydrology 260: 216–230. doi:10.1016/S0022-1694(01)00611-4.
  • Bishop, C. M. 2006. Pattern Recognition and Machine Learning. New York: Springer.
  • Candès, E., J. Romberg, and T. Tao. 2006. “Stable Signal Recovery from Incomplete and Inaccurate Measurements.” Communications on Pure and Applied Mathematics 8: 1207–1223. doi:10.1002/cpa.20124.
  • Candès, E., and M. Wakin. 2008. “An Introduction to Compressive Sampling.” IEEE Signal Processing Magazine 25 (2): 21–30. doi:10.1109/MSP.2007.914731.
  • Cascini, L., S. Cuomo, A. Mauro, M. Natale, S. Nocera, and F. Matano. 2021. “Multidisciplinary Analysis of Combined Flow-Like Mass Movements in a Catchment of Southern Italy.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 15 (1): 41–58. doi:10.1080/17499518.2019.1674339.
  • Cressie, N., and C. Wikle. 2011. Statistics for Spatio-Temporal Data. Hoboken: Wiley.
  • De Cesare, L., D. Myers, and D. Posa. 1997. “Spatial-Temporal Modeling of SO2 in Milan District.” In Geostatistics Wollongong’96, vol. 2, edited by E. Y. Baafi, and N. A. Schofield, 1031–1942. Dordrecht: Kluwer Academic.
  • De Cesare, L., D. Myers, and D. Posa. 2001a. “Estimating and Modeling Space-Time Correlation Structures.” Statistics & Probability Letters 51: 9–14. doi:10.1016/S0167-7152(00)00131-0.
  • De Cesare, L., D. Myers, and D. Posa. 2001b. “Product-sum Covariance for Space-Time Modeling: An Environmental Application.” Environmetrics 12: 11–23. doi:10.1002/1099-095X(200102)12:1<11::AID-ENV426>3.0.CO;2-P.
  • Dijk, A., H. Beck, R. Crosbie, R. Jeu, Y. Liu, G. Podger, B. Timbal, and N. Viney. 2013. “The Millennium Drought in Southeast Australia (2001-2009): Natural and Human Causes and Implications for Water Resources, Ecosystems, Economy, and Society.” Water Resources Research 49: 1040–1057. doi:10.1002/wrcr.20123.
  • Dimitrakopoulos, R., and X. Luo. 1994. “Spatiotemporal Modeling: Covariances and Ordinary Kriging Systems.” In Geostatistics for the Next Century, edited by R. Dimitrakopou-los, 88–93. Dordrecht: Kluwer Academic Publishers.
  • Donoho, D. 2006. “Compressed Sensing.” IEEE Transactions on Information Theory 52 (4): 1289–1306. doi:10.1109/TIT.2006.871582.
  • Estrela, T., and E. Vargas. 2012. “Drought Management Plans in the European Union: The Case of Spain.” Water Resource Management 26: 1537–1553. doi:10.1007/s11269-011-9971-2.
  • Faghmous, J., and V. Kuma. 2014. “Spatio-temporal Data Mining for Climate Data Advances, Challenges, and Opportunities.” Data Mining and Knowledge Discovery for Big Data 1: 83–116. doi:10.1007/978-3-642-40837-3_3.
  • GEO. 2021. “Geotechnical Engineering Office raingauge data.” https://www.geomap.cedd.gov.hk/GEOOpenData/eng/Raingauge.aspx.
  • Gräler, B., E. Pebesma, and G. Heuvelink. 2016. “Spatio-temporal Interpolation Using Gstat.” The R Journal 8 (1): 204–218. doi:10.32614/RJ-2016-014.
  • He, J., Y. Qiang, H. Luo, S. Zhou, and L. Zhang. 2021. “A Stress Test of Urban System Flooding upon Extreme Rainstorms in Hong Kong.” Journal of Hydrology 597: 125713. doi:10.1016/j.jhydrol.2020.125713.
  • HKO. 2021. “Hong Kong Observation.” https://www.hko.gov.hk/tc/index.html.
  • IPCC Adopted. 2014. “Climate Change 2014 Synthesis Report.”
  • Ji, S., D. Dunson, and L. Carin. 2009. “Multitask Compressive Sensing.” IEEE Transactions on Signal Processing 57 (1): 92–106. doi:10.1109/TSP.2008.2005866.
  • Ji, S., Y. Xue, and L. Carin. 2008. “Bayesian Compressive Sensing.” IEEE Transactions on Signal Processing 56: 2346–2356. doi:10.1109/TSP.2007.914345.
  • Kilibarda, M., T. Hengl, G. Heuvelink, B. Gräler, E. Pebesma, M. Tadić, and B. Bajat. 2014. “Spatio-temporal Interpolation of Daily Temperatures for Global Land Areas at 1 km Resolution.” Journal of Geophysical Research: Atmospheres 119 (5): 2294–2313. doi:10.1002/2013JD020803.
  • Kilibarda, M., M. Tadić, T. Hengl, J. Luković, and B. Bajat. 2015. “Global Geographic and Feature Space Coverage of Temperature Data in the Context of Spatio-Temporal Interpolation.” Spatial Statistics 14: 22–38. doi:10.1016/j.spasta.2015.04.005.
  • Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence 2 (12): 1137–1143. https://www.ijcai.org/Proceedings/95-2/Papers/016.pdf.
  • LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. doi:10.1109/5.726791.
  • Li, P., and Y. Wang. 2021. “Development of an Efficient Response Surface Method for Highly Nonlinear Systems from Sparse Sampling Data Using Bayesian Compressive Sensing.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7 (4): 04021050. doi:10.1061/AJRUA6.0001155.
  • Li, P., and Y. Wang. 2022. “An Active Learning Reliability Analysis Method Using Adaptive Bayesian Compressive Sensing and Monte Carlo Simulation (ABCS-MCS).” Reliability Engineering & System Safety 59: 108377. doi:10.1016/j.ress.2022.108377.
  • Liu, X., Y. Wang, and D. Li. 2020. “Numerical Simulation of the 1995 Rainfall-Induced Fei Tsui Road Landslide in Hong Kong: New Insights from Hydro-Mechanically Coupled Material Point Method.” Landslides 17: 2755–2775. doi:10.1007/s10346-020-01442-2.
  • Luo, J., L. Zhang, H. Yang, X. Wei, D. Liu, and J. Xu. 2022. “Probabilistic Model Calibration of Spatial Variability for a Physically-Based Landslide Susceptibility Model.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 4: 728–745. doi:10.1080/17499518.2021.1988986.
  • Ly, S., C. Charles, and A. Degré. 2011. “Geostatistical Interpolation of Daily Rainfall at Catchment Scale: The use of Several Variogram Models in the Ourthe and Ambleve Catchments, Belgium.” Hydrology and Earth System Sciences 15: 2259–2274. doi:10.5194/hess-15-2259-2011.
  • Lyu, B., Y. Hu, and Y. Wang. 2022. “Data-driven Development of Three-Dimensional Subsurface Models from Sparse Measurements Using Bayesian Compressive Sampling (BCS): A Benchmarking Study.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 9 (2): 04023010. https://ascelibrary.org/doi/abs/10.1061/AJRUA6.RUENG-935.
  • Ma, C. 2002. “Spatio-temporal Covariance Functions Generated by Mixtures.” Mathematical Geology 34: 965–975. doi:10.1023/A:1021368723926.
  • MathWorks, I. 2020. “MATLAB: The Language of Technical Computing.”
  • Medeiros, E., R. Lima, R. Olinda, L. Dantas, and C. Santos. 2019. “Space-time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS.” Water 11 (11): 2368. doi:10.3390/w11112368.
  • Montero, M., G. Fernández-Avilés, and J. Mateu. 2015. Spatial and Spatio-Temporal Geostatistical Modeling and Kriging. London: John Wiley & Sons Ltd.
  • Nester, T., A. Schöbel, U. Drabek, C. Rachoy, and H. Wiesenegger. 2008. “A Flood Warning System for Railways.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 2 (4): 237–249. doi:10.1080/17499510802199745.
  • Pati, Y., R. Rezaiifar, and P. Krishnaprasad. 1993. “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition.” In Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, 40–44. Pacific Grove: IEEE.
  • Phoon, K., J. Ching, and Y. Shuku. 2022a. “Challenges in Data-Driven Site Characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 114–126. doi:10.1080/17499518.2021.1896005.
  • Phoon, K., Y. Shuku, J. Ching, and Y. Ikumasa. 2022b. “Benchmarking Examples for Data-Driven Site Characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 4: 599–621. doi:10.1080/17499518.2022.2025541.
  • Porcu, E., and J. Mateu. 2007. “Mixture-based Modeling for Space-Time Data.” Environmetrics 18: 285–302. doi:10.1002/env.832.
  • Qiang, Y., L. Zhang, and T. Xiao. 2020. “Spatial-temporal Rain Field Generation for the Guangdong-Hong Kong-Macau Greater Bay Area Considering Climate Change.” Journal of Hydrology 583: 124584. doi:10.1016/j.jhydrol.2020.124584.
  • Qin, Z., T. Peng, V. Singh, and M. Chen. 2019. “Spatio-temporal Variations of Precipitation Extremes in Hanjiang River Basin, China, During 1960-2015.” Theoretical and Applied Climatology 138: 1767–1783. doi:10.1007/s00704-019-02932-7.
  • Rahmawati, N. 2020. “Space-time Variogram for Daily Rainfall Estimates Using Rain Gauges and Satellite Data in Mountainous Tropical Island of Bali, Indonesia (Preliminary Study).” Journal of Hydrology 590: 125177. doi:10.1016/j.jhydrol.2020.125177.
  • Rana, H., and G. Babu. 2022. “Probabilistic Back Analysis for Rainfall-Induced Slope Failure Using MLS-SVR and Bayesian Analysis.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. doi:10.1080/17499518.2022.2084555.
  • Rodríguez-Iturbe, I., and J. Mejía. 1974. “The Design of Rainfall Networks in Time and Space.” Water Resources Research 10 (4): 713–728. doi:10.1029/WR010i004p00713.
  • Rouhani, S., and T. Hall. 1989. “Space-time Kriging of Groundwater Data.” In Geostatistics, edited by M. Armstrong, 639–651. Dordrecht: Kluwer Academic.
  • Sharma, P. 2018. “Rainfall Flood Hazard at Nuclear Power Plants in India.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 12 (3): 218–233. doi:10.1080/17499518.2018.1426866.
  • Sivia, D. S., and J. Skilling. 2006. Data Analysis: A Bayesian Tutorial. New York, NY: Oxford University Press.
  • Spadavecchia, L., and M. Williams. 2009. “Can Spatio-Temporal Geostatistical Methods Improve High Resolution Regionalisation of Meteorological Variables?” Agricultural and Forest Meteorology 149: 1105–1117. doi:10.1016/j.agrformet.2009.01.008.
  • Stallkamp, J., M. Schlipsing, J. Salmen, and C. Igel. 2012. “Man vs. Computer: Benchmarking Machine Learning Algorithms for Traffic Sign Recognition.” Neural Networks 32: 323–332. doi:10.1016/j.neunet.2012.02.016.
  • Swiss, R. 2013. Mind the Risk: A Global Ranking of Cities Under Threat from Natural Disasters. Cham: Swiss Reinsurance Company Ltd.
  • Tang, D., D. Li, and Z. Cao. 2017. “Slope Stability Analysis in the Three Gorges Reservoir Area Considering Effect of Antecedent Rainfall.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 11 (2): 161–172. doi:10.1080/17499518.2016.1193205.
  • Tonini, M., G. Pecoraro, K. Romailler, and M. Calvello. 2022. “Spatio-temporal Cluster Analysis of Recent Italian Landslides.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (3): 536–554. doi:10.1080/17499518.2020.1861634.
  • Tropp, J., and A. Gilbert. 2007. “Signal Recovery from Random Measurements via Orthogonal Matching Pursuit.” IEEE Transactions on Information Theory 53 (12): 4655–4666. doi:10.1109/TIT.2007.909108.
  • Varouchakis, E., P. Theodoridou, and G. Karatzas. 2019. “Spatiotemporal Geostatistical Modeling of Groundwater Levels Under a Bayesian Framework Using Means of Physical Background.” Journal of Hydrology 575: 487–498. doi:10.1016/j.jhydrol.2019.05.055.
  • Vehtari, A., A. Gelman, and J. Gabry. 2017. “Practical Bayesian Model Evaluation Using Leave-one-out Cross-Validation and WAIC.” Statistics and Computing 27: 1413–1432. doi:10.1007/s11222-016-9696-4.
  • Wang, Y., O. V. Akeju, and T. Zhao. 2017. “Interpolation of Spatially Varying but Sparsely Measured geo-Data: A Comparative Study.” Engineering Geology 231: 200–217. doi:10.1016/j.enggeo.2017.10.019.
  • Wang, Y., Z. Guan, and T. Zhao. 2019a. “Sample Size Determination in Geotechnical Site Investigation Considering Spatial Variation and Correlation.” Canadian Geotechnical Journal 56 (7): 992–1002. doi:10.1139/cgj-2018-0474.
  • Wang, Y., Y. Hu, and K. Phoon. 2021. “Non-parametric Modelling and Simulation of Spatiotemporally Varying Geo-Data.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 77–97. doi:10.1080/17499518.2021.1971258.
  • Wang, Y., and P. Li. 2021. “Data-driven Determination of Sample Number and Efficient Sampling Locations for Geotechnical Site Investigation of a Cross-Section Using Voronoi Diagram and Bayesian Compressive Sampling.” Computers and Geotechnics 130: 103898. doi:10.1016/j.compgeo.2020.103898.
  • Wang, Y., and T. Zhao. 2016. “Interpretation of Soil Property Profile from Limited Measurement Data: A Compressive Sampling Perspective.” Canadian Geotechnical Journal 53 (9): 1547–1559. doi:10.1139/cgj-2015-0545.
  • Wang, Y., and T. Zhao. 2017. “Statistical Interpretation of Soil Property Profiles from Sparse Data Using Bayesian Compressive Sampling.” Géotechnique 67 (6): 523–536. doi:10.1680/jgeot.16.P.143.
  • Wang, Y., T. Zhao, Y. Hu, and K. K. Phoon. 2019b. “Simulation of Random Fields with Trend from Sparse Measurements Without Detrending." Journal of Engineering Mechanics 145 (2): 04018130. doi:10.1061/(ASCE)EM.1943-7889.0001560.
  • Wang, Y., T. Zhao, and K. K. Phoon. 2018. “Direct Simulation of Random Field Samples from Sparsely Measured Geotechnical Data with Consideration of Uncertainty in Interpretation." Canadian Geotechnical Journal 55 (6): 862–880. doi:10.1139/cgj-2017-0254.
  • Wang, Y., T. Zhao, and K. K. Phoon. 2019c. “Statistical Inference of Random Field Auto-Correlation Structure from Multiple Sets of Incomplete and Sparse Measurements Using Bayesian Compressive Sampling-Based Bootstrapping.” Mechanical Systems and Signal Processing 124: 217–236. doi:10.1016/j.ymssp.2019.01.049.
  • Williams, D., B. Nelsen, C. Berrett, G. Williams, and T. Moon. 2018. “A Comparison of Data Imputation Methods Using Bayesian Compressive Sensing and Empirical Mode Decomposition for Environmental Temperature Data.” Environmental Modelling & Software 102: 172–184. doi:10.1016/j.envsoft.2018.01.012.
  • Wright, D., J. Smith, and M. Baeck. 2014. “Flood Frequency Analysis Using Radar Rainfall Fields and Stochastic Storm Transposition.” Water Resource Research 50 (2): 1592–1615. doi:10.1002/2013WR014224.
  • Yuan, J., I. Papaioannou, and D. Straub. 2019. “Probabilistic Failure Analysis of Infinite Slopes Under Random Rainfall Processes and Spatially Variable Soil.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 13 (1): 20–33. doi:10.1080/17499518.2018.1489059.
  • Zhao, T., Y. Hu, and Y. Wang. 2018. “Statistical Interpretation of Spatially Varying 2D geo-Data from Sparse Measurements Using Bayesian Compressive Sampling.” Engineering Geology 246: 162–175. doi:10.1016/j.enggeo.2018.09.022.
  • Zhao, T., and Y. Wang. 2018. “Interpretation of Pile Lateral Response from Deflection Measurement Data: A Compressive Sampling-Based Method.” Soils and Foundations 58 (4): 957–971. doi:10.1016/j.sandf.2018.05.002.
  • Zhao, T., and Y. Wang. 2019. “Determination of Efficient Sampling Locations in Geotechnical Site Characterization Using Information Entropy and Bayesian Compressive.” Canadian Geotechnical Journal 56 (11): 1622–1637. doi:10.1139/cgj-2018-0286.
  • Zhao, T., and Y. Wang. 2020. “Non-parametric Simulation of non-Stationary non-Gaussian 3D Random Field Samples Directly from Sparse Measurements Using Signal Decomposition and Markov Chain Monte Carlo.” Reliability Engineering and System Safety 203: 107087. doi:10.1016/j.ress.2020.107087.
  • Zhao, T., and Y. Wang. 2021. “Statistical Interpolation of Spatially Varying but Sparsely Measured 3D geo-Data Using Compressive Sensing and Variational Bayesian Inference.” Mathematical Geosciences 53 (6): 1171–1199. doi:10.1007/s11004-020-09913-x.
  • Zhou, S., L. Gao, and L. Zhang. 2019. “Predicting Debris-Flow Clusters Under Extreme Rainstorms: A Case Study on Hong Kong Island.” Bulletin of Engineering Geology and the Environment 78: 5775–5794. doi:10.1007/s10064-019-01504-3.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.