Is Clustering Time-Series Water Depth Useful? An Exploratory Study for Flooding Detection in Urban Drainage Systems
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Robert Sitzenfrei | Simon Brewer | Jiada Li | Daniyal Hassan | R. Sitzenfrei | Jiada Li | Simon C. Brewer | D. Hassan
[1] Alessandro Laio,et al. Clustering by fast search and find of density peaks , 2014, Science.
[2] T. Chang,et al. Inundation simulation for urban drainage basin with storm sewer system , 2000 .
[3] Lu Xing,et al. Unsteady pressure patterns discovery from high-frequency sensing in water distribution systems. , 2019, Water research.
[4] Donald W. Bouldin,et al. A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[6] Eamonn J. Keogh,et al. Clustering of time-series subsequences is meaningless: implications for previous and future research , 2004, Knowledge and Information Systems.
[7] Luca Carniello,et al. Multipurpose Use of Artificial Channel Networks for Flood Risk Reduction: The Case of the Waterway Padova–Venice (Italy) , 2020, Water.
[8] Md. Jalil Piran,et al. Survey of computational intelligence as basis to big flood management: challenges, research directions and future work , 2018 .
[9] Hidetoshi Shimodaira,et al. Pvclust: an R package for assessing the uncertainty in hierarchical clustering , 2006, Bioinform..
[10] T. Palmer,et al. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .
[11] Avi Ostfeld,et al. Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions , 2014, Environ. Model. Softw..
[12] T. Caliński,et al. A dendrite method for cluster analysis , 1974 .
[13] Andrea Rinaldo,et al. GEOMORPHOLOGICAL THEORY OF THE HYDROLOGICAL RESPONSE , 1996 .
[14] Meng Li,et al. Automatic setting of urban drainage pipe monitoring points based on scenario simulation and fuzzy clustering , 2018, Urban Water Journal.
[15] Xiangyu Li,et al. Davies Bouldin Index based hierarchical initialization K-means , 2017, Intell. Data Anal..
[16] Pao-Shan Yu,et al. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting , 2017 .
[17] Abhiram Mullapudi,et al. Deep reinforcement learning for the real time control of stormwater systems , 2020 .
[18] Luca Carniello,et al. Simplified methods for real-time prediction of storm surge uncertainty: The city of Venice case study , 2014 .
[19] Kwok-wing Chau,et al. Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.
[20] Jiada Li,et al. Rethinking the Framework of Smart Water System: A Review , 2020, Water.
[21] Branko Kerkez,et al. Are all data useful? Inferring causality to predict flows across sewer and drainage systems using directed information and boosted regression trees. , 2018, Water research.
[22] Mustafa Neamah Jebur,et al. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .
[23] Zaher Mundher Yaseen,et al. An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction , 2019, Journal of Hydrology.
[24] Rui Xu,et al. Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.
[25] Xiaohong Chen,et al. Flood hazard risk assessment model based on random forest , 2015 .
[26] Jiada Li. A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems. , 2020, The Science of the total environment.
[27] Wolfgang Rauch,et al. Optimizing Small Hydropower Systems in Water Distribution Systems Based on Long-Time-Series Simulation and Future Scenarios , 2015 .
[28] Antonella Sanna,et al. A data assimilation procedure for operational prediction of storm surge in the northern Adriatic Sea , 2006 .
[29] Shuming Liu,et al. Burst Detection by Analyzing Shape Similarity of Time Series Subsequences in District Metering Areas , 2020 .
[30] Shahaboddin Shamshirband,et al. Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran , 2018 .
[31] Patricio A. Vela,et al. A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..
[32] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[33] G. Freni,et al. Optimal water quality sensor positioning in urban drainage systems for illicit intrusion identification , 2019, Journal of Hydroinformatics.
[34] Tao Tao,et al. Construction Cost-Based Effectiveness Analysis of Green and Grey Infrastructure in Controlling Flood Inundation: A Case Study , 2019, Journal of Water Management Modeling.
[35] William D. Shannon,et al. 11 Cluster Analysis , 2007 .
[36] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[37] Kevin Horsburgh,et al. Development and evaluation of an ensemble forecasting system for coastal storm surges , 2010 .
[38] Age K. Smilde,et al. Principal Component Analysis , 2003, Encyclopedia of Machine Learning.
[39] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[40] M. Borga,et al. Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins , 2008 .
[41] Ujjwal Maulik,et al. Performance Evaluation of Some Clustering Algorithms and Validity Indices , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[42] D Butler,et al. Clustering analysis of water distribution systems: identifying critical components and community impacts. , 2014, Water science and technology : a journal of the International Association on Water Pollution Research.
[43] Yingjie Tian,et al. A Comprehensive Survey of Clustering Algorithms , 2015, Annals of Data Science.
[44] Aaron Poresky,et al. Smarter Stormwater Systems. , 2016, Environmental science & technology.
[45] Moh'd Belal Al Zoubi,et al. An Efficient Approach for Computing Silhouette Coefficients , 2008 .
[46] C. Shu,et al. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system , 2008 .
[47] Ying Wah Teh,et al. Time-series clustering - A decade review , 2015, Inf. Syst..
[48] Piero Lionello,et al. High resolution climate projection of storm surge at the Venetian coast , 2013 .
[49] Hamid Darabi,et al. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. , 2018, The Science of the total environment.
[50] Brandon P. Wong,et al. Adaptive measurements of urban runoff quality , 2016 .
[51] P. Danielsson. Euclidean distance mapping , 1980 .
[52] Avi Ostfeld,et al. Data-driven modelling: some past experiences and new approaches , 2008 .
[53] Luca Carniello,et al. Optimal floodgate operation for river flood management: The case study of Padova (Italy) , 2020, Journal of Hydrology: Regional Studies.
[54] P. Willems,et al. A Methodology for the Design of RTC Strategies for Combined Sewer Networks , 2018, Water.
[55] Kwok-wing Chau,et al. Design of water distribution systems using an intelligent simple benchmarking algorithm with respect to cost optimization and computational efficiency , 2019, Water Supply.
[56] Xue Wu,et al. Burst detection in district metering areas using a data driven clustering algorithm. , 2016, Water research.
[57] Mudasser Iqbal,et al. Automated sub-zoning of water distribution systems , 2014, Environ. Model. Softw..
[58] Dan Koo,et al. Towards Sustainable Water Supply: Schematic Development of Big Data Collection Using Internet of Things (IoT) , 2015 .
[59] Marcelo Horacio Garcia,et al. Innovative modeling framework for combined sewer overflows prediction , 2017 .
[60] B. Russo,et al. Real-time urban flood forecasting and modelling – a state of the art , 2013 .
[61] Ming Ye,et al. Using cluster analysis for understanding spatial and temporal patterns and controlling factors of groundwater geochemistry in a regional aquifer , 2020 .
[62] Li-Chiu Chang,et al. Clustering-based hybrid inundation model for forecasting flood inundation depths , 2010 .
[63] Davar Khalili,et al. Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions , 2010 .
[64] R. L. Thorndike. Who belongs in the family? , 1953 .
[65] ChangKyoo Yoo,et al. Determination of key sensor locations for non-point pollutant sources management in sewer network , 2013, Korean Journal of Chemical Engineering.
[66] Avi Ostfeld,et al. Topological clustering for water distribution systems analysis , 2011, Environ. Model. Softw..
[67] E G Knox. Epidemiology of prenatal infections: an extension of the congenital rubella model. , 1983, Statistics in medicine.
[68] M. B. Abbott,et al. Twenty-Five Years of Hydroinformatics , 2017 .
[69] Mohamed M. Morsy,et al. Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks , 2019, Water.
[70] M. Forina,et al. Clustering with dendrograms on interpretation variables , 2002 .