Computational approaches for annual maximum river flow series

Tiwari, Harinarayan; Pd., Subash; Sharma, Nayan; Kumar, Deepak

doi:10.1016/j.asej.2015.07.016

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…In the AMS, the largest event in each year is extracted and recorded in a series that contains critical information such as extreme precipitation or peak flow amount. These data are easily obtained and widely used in hydrological statistical analysis [14][15][16][17]. However, the AMS extracts only the largest event, and secondary events occurring in one year may exceed the annual maximum of other years.…”

Section: Introductionmentioning

confidence: 99%

Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China

Shao

Zhao

et al. 2021

Water

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Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the optimum distribution for each region is determined by an integrative assessment. Second, underestimation of quantiles and the applicability of Chow’s equation are verified. The results show that quantiles are underestimated based on the annual maximum series, and that Chow’s formula is applicable for the study area. Next, two methods are used to correct the underestimation of frequency estimation. A set of rational and reliable frequency estimations is obtained using the regional L-moments method and the two revised methods, which can indirectly provide a robust basis for flood control and water resource management. This study extends previous works by verifying underestimation of the quantiles and the provision of two improved methods for obtaining reliable quantile estimations of extreme precipitation at lower recurrence intervals, especially in solving reliable estimates for a 1-year return period from the integral lower limit of the frequency distribution.

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Section: Introductionmentioning

confidence: 99%

Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China

Shao

Zhao

et al. 2021

Water

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“…In the past few decades, reliable prediction of rivers inflow has gained popularity in all water-related departments because of their crucial role in the reservoir, irrigation management, water planning, risk evaluation and flood controlling (Porporato & Ridolfi, 2001;Jandhyala, Liu & Fotopoulos, 2009;Di, Yang & Wang, 2014;Tiwari et al, 2017;Nazir et al, 2019). Johnston & Smakhtin (2014) reviewed the importance of river data and concluded that river inflow data is an indispensable component of water resources.…”

Section: Introductionmentioning

confidence: 99%

“…Several data-driven approaches have been recognized as useful tools to model complex non-stationary and non-linear river inflow data. For example, K-Nearest Neighbors, model tree (Oyebode, Otieno & Adeyemo, 2014), computational intelligence (Das & Ghosh, 2017), Genetic Algorithm, Support Vector Machine, Neural Networks (NN) includes Artificial Neural Network (ANN) and Artificial Intelligence (Tiwari et al, 2017). These data-driven models can learn complex behavior, which is an inherent part of river inflow data, without considering any assumption about data.…”

Section: Introductionmentioning

confidence: 99%

Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

Nazir

Hussain

Faisal

et al. 2020

PeerJ

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Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

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