Discovering Flood Recession Pattern in Hydrological Time Series Data Mining during the Post Monsoon Period

Mishra, Sanjay; Chandran, Saravanan; Dwivedi, V. K.; Pathak, Khanindra

doi:10.5120/15597-4375

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…In the paper, the number of classes was determined based on the analysis of geometry of the dendrogram and the plot of the linkage distance curve. The presented methods are commonly used in hydrometeorological research [51][52][53][54][55][56][57][58][59][60]. The mathematical-statistical processing of the analysis results employed statistical procedures included in the Excel (Microsoft) and Statistica 13 (TIBCO Software Inc.) software.…”

Section: Of 19mentioning

confidence: 99%

Links between Teleconnection Patterns and Water Level Regime of Selected Polish Lakes

Plewa

Perz

Wrzesiński

2019

Water

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The paper identifies relationships between lake water levels and indices of macroscale atmospheric circulations: Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic (EA), and Scandinavian pattern (SCAND). Correlation coefficients between synchronous and asynchronous series of monthly water levels and 4 circulation indices were calculated. Based on Ward hierarchical grouping considering 156 correlation coefficients, the groups of lakes were designated due to the strength and term of relation of circulation indices with lake water levels. It was found that these links are not strong but noticeable. The strength of relationships varies in space and time, and the designated groups of lakes refer not only to the climatic diversity of the studied area, but also to some extent to the types of water levels regime. The observed relationships are the most important in the case of AO and NAO (particularly in winter period), and slightly weaker for EA and SCAND. The analysis used mean monthly water levels of 15 lakes in northern Poland from 1976–2015.

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Section: Of 19mentioning

confidence: 99%

Links between Teleconnection Patterns and Water Level Regime of Selected Polish Lakes

Plewa

Perz

Wrzesiński

2019

Water

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“…In time-based clustering, a nonlinear dissimilarity measure is commonly considered. A common nonlinear mapping dissimilarity measure used by hydrologists for ordered time sequence data is Dynamic Time Warping (DTW) dissimilarity measure (Gertseema et al, 2016;Gupta & Chaturvedi, 2013;Mishra et al, 2015). This nonlinear dissimilarity measure aligns each data points elastically which allows similar shapes to match when the points are out of phase in a time series sequence.…”

Section: The International Seminar On Mathematics In Industry (Ismi) mentioning

confidence: 99%

Assessing Dynamic-Time-Warping Dissimilarity Measures in Regionalization of River Discharges

Mansor

Ahmad

Heryansyah

2019

MJS

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Regionalization of river discharges is a process of transferring hydrological information to generalize hydrological information from one river to another. One approach to regionalize river discharge is to use a distance-based regional analysis by employing a Dynamic Time Warping (DTW) dissimilarity measure to cluster homogeneous river discharge patterns based on sequenced of time series discharge data. However, clustering homogeneous river discharge patterns can be sensitive to the choice of distance metric measures used due to out of phase behavior in the discharge time series. In this study, we assess three types of Dynamic Time Warping (DTW) measures specifically conventional DTW, a feature based DTW and a weighted based DTW on four annual discharge time series from six rivers in the state of Johor, Malaysia. A comparison of eight different clustering validation indices to determine the optimal number of rivers clusters with similar discharge patterns. These indices are used to measure the internal and external strength of the identified clusters. The results indicate that weighted based DTW outperform the conventional DTW and feature based DTW with 75% of the clustering indices agree that there are three optimal clusters of river discharge. By using weight as a function in DTW, it helps to cater the out of phase behavior in river discharge time series with the highest agreement of clustering indices compared to other types of DTW measures. We also found that three of the rivers (Sayong, Bekok, and Segamat) have similar river discharge patterns and could be used together in the generalization process. Meanwhile, the other rivers (Johor, Kahang, and Muar) varies in their time series patterns.

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“…Mishra et al (2013) presented the analysis based on data mining technique in hydrological daily discharge time series of the panchratna station in the river Brahmaputra under Brahmaputra and Barak Basin Organization in India. Kmeans, Dynamic Time Wrapping (DTW), and agglomerative hierarchical clustering are used to cluster and discover the discharge pattern in terms of the modeling [8,9,10] . A paper on data mining, using SVM approach for large number of algorithm and based on the experiments, analysed the results.…”

Section: Introductionmentioning

confidence: 99%

Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

Choubey¹,

Mishra²,

Pandey³

2014

IJCA

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This study presents support vector machine based model for forecasting the runoff-rainfall events. A SVM based model is either implemented through Radial base or Gaussian based Kernel functions. SVM uses precipitation, temperature, sediment, rainfall, water level and discharge as input variable parameters. In this research the Sequential minimal optimization algorithm (SMO) has been implemented as an effective method for training support vector machines (SVMs) on classification tasks defined on large and sparse real time data sets. In this work, we generalized the SMO so that it can handle regression problem and by dividing datasets into test data and trained data performed future forecasting keeping four major evaluation parameters Root Mean Square Error (RMSE), Mean Absolute error (MAE), Mean Squared error (MSE) and correlation coefficient (CC). Study site for this research is Narmada basin reservoir hosahangabad catchment area and the experimental results on predicting the full natural flow of Narmada River indicates that support vector machine method performs far better and more accurate from the current forecasting practices (Artificial Neural Network).

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Discovering Flood Recession Pattern in Hydrological Time Series Data Mining during the Post Monsoon Period

Cited by 8 publications

References 35 publications

Links between Teleconnection Patterns and Water Level Regime of Selected Polish Lakes

Links between Teleconnection Patterns and Water Level Regime of Selected Polish Lakes

Assessing Dynamic-Time-Warping Dissimilarity Measures in Regionalization of River Discharges

Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

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