Identifying Regional Models for Flow Duration Curves with Evolutionary Polynomial Regression: Application for Intermittent Streams

Costa, Veber Afonso Figueiredo; Fernandes, Wilson; Starick, Ângelo

doi:10.1061/(asce)he.1943-5584.0001873

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Like other statistical methods (Burgan and Aksoy 2022), this paper also has some limitations. For example, the selection of some explanatory variables may have some subjectivity and uncertainty (Veber Costa 2020). Secondly, the choose of distribution function may have a certain impact on the results due to different research locations and local hydrological conditions (Li et al 2010), needed to be compared and verified to find the most suitable one for the region (Manuel Almeida 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The extensive hydrologic research focused on regionalization studies indicates the global hydrologic issue of limited hydrometric data, which is evident in the worldwide distribution of regionalization studies. They are mainly distributed in the United States (Over et al 2018; Zhang 2017; Cheng et al 2012; Singh 1971; Blum et al 2017), Brazil (Veber Costa 2020; Silva 2019; Pessoa et al 2021), Italy (Mancini 2016; Pumo et al 2014; Mendicino and Senatore 2013), Canada (Requena et al 2018), Africa (Manuel Almeida 2021), Turkey (Burgan and Aksoy 2022), Taiwan (Paoshan Yu 1996), Himalayan region (Rees et al 2002), Zimbabwe (Mazvimavi et al 2004), Australia (Zhang et al 2015). Currently, there is a lack of research on FDCs applicable to hydrological conditions in China.…”

Section: Discussionmentioning

confidence: 99%

“…The regional patterns of FDCs across continental United States was investigated and found that the dominant controlling factors on the FDCs vary regionally in different catchments (Ye et al 2012). Regional models of FDC for the parameters of the distributions suitable for situations where arid areas and intermittent rivers commonly exist were identified with the evolutionary polynomial regression (EPR) technique (Veber Costa 2020). In our study, the pattern and control factors in the middle and lower Yangtze River Basin share similarities with these spatial patterns of watersheds for the control of climate and geographical characteristics on the shape of the FDC, which can extract characteristics of the climate and geography of a fixed watershed from the distributional parameters.…”

Section: Discussionmentioning

confidence: 99%

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Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

Zhou,

Zhang

2024

Hydrological Processes

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The flow duration curve (FDC) is the cumulative distribution function, which represents the relationship between the frequency and magnitude of streamflow, and the precipitation duration curves (PDC) follows the same principle. Nowadays, the correlation between the shape of PDC and FDCs, their respective physical control factors including human activities, and their fitting conditions in unmeasured catchments across China have not been fully understood. In this paper, daily precipitation and streamflow data with 30 years records from 224 hydrological stations in the middle and lower Yangtze River basin were chosen to fit PDC and FDCs through gamma distribution. Framework was proposed for modelling FDCs to analyse the relationship, similarity, regional patterns and response mechanism of fitting parameters between PDC and FDCs, dividing the streamflow time series into fast and base flow and stations into three categories in consideration of human activities to attribute the shapes of PDC and FDCs to catchment meteorological and geographical characteristics and physical processes under natural conditions. Results indicate that the parameters of PDC and certain FDCs (TFDC, FFDC, SFDC) share similar spatial patterns but vary for the different duration and interactions of the processes. The climate and catchment characteristics such as extreme properties of precipitation, base flow index (BFI), Pmax*αp and concentration ratio index (CIM) will influence the shape of PDC and FDCs. The relationship between BFI and SFDC/TFDC can be better reflected in “Regulated watersheds”, while CIM/Pmax*αp and PDC/FFDC in “watersheds in the mainstream” are more related. This paper provides a deeper understanding and more accurate way of the application of these parameters in describing and predicting hydrological processes and estimation of PDC and FDCs in unmeasured catchments, and can be applied to more future research about processes based on catchment rainfall‐runoff responses and physical controls.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

Zhou,

Zhang

2024

Hydrological Processes

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“…Sensitivity analysis is an essential process for differentiating (gray) EPR models from black-box approaches. For these reasons, EPR models have found its way into engineering practice (Ahangar-Asr et al, 2011b;Alani and Faramarzi, 2014;Balf et al, 2018;Berardi et al, 2008;Bruno et al, 2018;Costa et al, 2020;Doglioni et al, 2010;Doglioni and Simeone, 2021;Faramarzi et al, 2012;Fiore et al, 2012Fiore et al, , 2016Giustolisi et al, 2007Giustolisi et al, , 2008Gomes et al, 2021a;Jin and Yin, 2020;Laucelli and Giustolisi, 2011;Montes et al, 2020;Rezania et al, 2008Rezania et al, , 2010Rezania et al, , 2011Shahin, 2015).…”

Section: Introductionmentioning

confidence: 99%

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

Gomes

Vrugt

2022

Engineering Applications of Artificial Intelligence

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“…In this study, intermittent flow data at a gaging station are viewed as a 'mixture-type' data comprising of both categorical (flow, no-flow) and continuous (non-zero flows) data types (Eris et al, 2018;Costa et al, 2020). It is therefore hypothesized that a combined classification-regression machine learning approach is likely to provide better results than models that treat the intermittent flow data as a continuum.…”

mentioning

confidence: 99%

Deep, Wide, or Shallow? Artificial Neural Network Topologies for Predicting Intermittent Flows

Forghanparast

Hernandez

Uddameri

2021

Preprint

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Abstract. Intermittent Rivers and Ephemeral Streams (IRES) comprise 60 % of all streams in the US and about 50 % of the streams worldwide. Furthermore, climate-driven changes are expected to force a shift towards intermittency in currently perennial streams. Most modeling studies have treated intermittent streamflows as a continuum. However, it is better to envision flow data of IRES as a “mixture-type”, comprised of both flow and no-flow regimes. It is therefore hypothesized that data-driven models with both classification and regression cells can improve the streamflow forecasting abilities in these streams. Deep and wide Artificial Neural Networks (ANNs) comprising of classification and regression cells were developed here by stacking them in series and parallel configurations. These deep and wide network architectures were compared against the commonly used single hidden layer ANNs (shallow), as a baseline, for modeling IRES flow series under the continuum assumption. New metrics focused on no-flow persistence and transitions between flow and no-flow states were formulated using contingency tables and Markov chain analysis. Nine IRES across the state of Texas, US, were used as a wide range of testbeds with different hydro-climatic characteristics. Model overfitting and the curse-of-dimensionality were reduced using extreme learning machines (ELM), and balancing training data using the synthetic minority oversampling technique (SMOTE), greedy learning and Least Absolute Shrinkage and Selection Operator (LASSO). The addition of classifier cells greatly improved the ability to distinguish between no-flow and flow states, in turn, improving the ability to capture no-flow persistence (dryness) and transitions to and from flow states (dryness initiation and cessation). The wide network topology provided better results when the focus was on capturing low flows and the deep topology did well in capturing extreme flows (zero and > 75th percentile).

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Identifying Regional Models for Flow Duration Curves with Evolutionary Polynomial Regression: Application for Intermittent Streams

Cited by 9 publications

References 27 publications

Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

Deep, Wide, or Shallow? Artificial Neural Network Topologies for Predicting Intermittent Flows

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