Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering

Srinivas, V. V.; Tripathi, Shivam; Rao, A. Ramachandra; Govindaraju, Rao S.

doi:10.1016/j.jhydrol.2007.09.046

Cited by 130 publications

(80 citation statements)

References 46 publications

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“…In the recent years, SOMs were also successfully applied for catchments classification either based on geo-morphoclimatic descriptors (Hall and Minns, 1999;Hall et al, 2002;Srinivas et al, 2008;Di Prinzio et al, 2011) or based on hydrological signatures (Chang et al, 2008;Ley et al, 2011;Toth, 2013); however, it is important to underline that the clustering is not carried out here in order to identify a pooling group of similar catchments for developing a region- specific model, but for the optimal division of the available data for the parameterisation and independent testing of a single model to be applied over the entire study area.…”

Section: Identification Of Balanced Cross-validation Subsets With Sommentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

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Abstract. In many real-world flood forecasting systems, the runoff thresholds for activating warnings or mitigation measures correspond to the flow peaks with a given return period (often 2 years, which may be associated with the bankfull discharge). At locations where the historical streamflow records are absent or very limited, the threshold can be estimated with regionally derived empirical relationships between catchment descriptors and the desired flood quantile. Whatever the function form, such models are generally parameterised by minimising the mean square error, which assigns equal importance to overprediction or underprediction errors.Considering that the consequences of an overestimated warning threshold (leading to the risk of missing alarms) generally have a much lower level of acceptance than those of an underestimated threshold (leading to the issuance of false alarms), the present work proposes to parameterise the regression model through an asymmetric error function, which penalises the overpredictions more.The estimates by models (feedforward neural networks) with increasing degree of asymmetry are compared with those of a traditional, symmetrically trained network, in a rigorous cross-validation experiment referred to a database of catchments covering the country of Italy. The analysis shows that the use of the asymmetric error function can substantially reduce the number and extent of overestimation errors, if compared to the use of the traditional square errors. Of course such reduction is at the expense of increasing underestimation errors, but the overall accurateness is still acceptable and the results illustrate the potential value of choosing an asymmetric error function when the consequences of missed alarms are more severe than those of false alarms.

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Section: Identification Of Balanced Cross-validation Subsets With Sommentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

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“…In the recent years, also non-supervised neural networks, and in particular of the SOM (self-organising mapping) type, were successfully applied (and sometimes compared with other methods such as K-means or Fuzzy C-means) for catchments classification purposes (Hall and Minns, 1999;Hall et al, 2002;Jingyi and Hall, 2004;Chang et al, 2008;Srinivas et al, 2008;Di Prinzio et al, 2011;Ley et al, 2011). SOM-type neural networks learn to cluster the input data by recognizing different patterns organising the data on the basis of their similarity, quantified by means of a distance measure (in the present case, like in the majority of applications, the Euclidean distance).…”

Section: Classification Of Streamflow Signatures With Som Neural Netwmentioning

confidence: 99%

Catchment classification based on characterisation of streamflow and precipitation time series

Toth

2013

Hydrol. Earth Syst. Sci.

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Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process.The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, obtaining groups of catchments with a clear hydrological distinctiveness, as highlighted by the identification of the main patterns of the input variables in the different classes and the interpretation of their interrelations. In addition, even if no geographical, morphological nor climatological information is provided in input to the SOM network, the clusters exhibit an overall consistency as far as location, altitude and precipitation regime are concerned.In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesise the variability and correlation properties of the precipitation time series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally assigns the ungauged catchments, through a leave-one-out cross validation, to one of the above identified hydrologic response classes. The approach delivers a quite satisfactory identification of the membership of ungauged catchments to the streamflow-based classes, since the comparison of the two cluster sets shows a misclassification rate of around 20 %.Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time series may be a promising way for better representing the hydrologic and climatic character of the study catchments.

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“…Recently, with availability of archives on a variety of variables depicting hydrology, climatology, topography, land use/land cover and soils, and advent of powerful computational facilities, cluster analysis techniques gained recognition for regionalization owing to their effectiveness in interpreting patterns in multivariate data sets [e.g., Jingyi and Hall, 2004;Shu and Burn, 2004;Rao and Srinivas, 2008;Srinivas et al, 2008]. Cluster analysis is the generic name of a variety of multivariate statistical procedures that are used to…”

Section: Introductionmentioning

confidence: 99%

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

Basu

Srinivas

2014

Water Resources Research

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Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites.

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Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering

Cited by 130 publications

References 46 publications

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Catchment classification based on characterisation of streamflow and precipitation time series

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

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