Simplified approach for flood estimation and propagation

Drobot, Radu; Dinu, Cristian; Drăghia, Aurelian Florentin; Adler, Mary-Jeanne; Corbuș, Ciprian; Mătreaţă, Marius

doi:10.1109/aqtr.2014.6857921

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…In order to derive the final equations for the DFDI, the ''Eureqa Formulize'' software developed by Schmidt and Lipson [2009] was chosen to derive explicit mathematical equations between independent and dependent variables. In recent years, this software has been utilized in a variety of environmental issues as a reliable and accurate tool to evaluate symbolic regression-based problems [Abrahart and Beriro, 2012;Drobot et al, 2014]. Five main mathematical operators, exponential, trigonometry, and delay functions to derive the optimum mathematical equations subject to a variety of the regression complexities have been considered.…”

Section: Deriving Mathematical Equations For Savi and Sawaimentioning

confidence: 99%

A data fusion‐based drought index

Azmi

Rüdiger

Walker

2016

Water Resources Research

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Drought and water stress monitoring plays an important role in the management of water resources, especially during periods of extreme climate conditions. Here, a data fusion‐based drought index (DFDI) has been developed and analyzed for three different locations of varying land use and climate regimes in Australia. The proposed index comprehensively considers all types of drought through a selection of indices and proxies associated with each drought type. In deriving the proposed index, weekly data from three different data sources (OzFlux Network, Asia‐Pacific Water Monitor, and MODIS‐Terra satellite) were employed to first derive commonly used individual standardized drought indices (SDIs), which were then grouped using an advanced clustering method. Next, three different multivariate methods (principal component analysis, factor analysis, and independent component analysis) were utilized to aggregate the SDIs located within each group. For the two clusters in which the grouped SDIs best reflected the water availability and vegetation conditions, the variables were aggregated based on an averaging between the standardized first principal components of the different multivariate methods. Then, considering those two aggregated indices as well as the classifications of months (dry/wet months and active/non‐active months), the proposed DFDI was developed. Finally, the symbolic regression method was used to derive mathematical equations for the proposed DFDI. The results presented here show that the proposed index has revealed new aspects in water stress monitoring which previous indices were not able to, by simultaneously considering both hydrometeorological and ecological concepts to define the real water stress of the study areas.

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Section: Deriving Mathematical Equations For Savi and Sawaimentioning

confidence: 99%

A data fusion‐based drought index

Azmi

Rüdiger

Walker

2016

Water Resources Research

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“…In this study we used "Eureqa Formulize" software [47] to form SRs. Recently this software has been increasingly applied by researchers in different environmental studies as a reliable tool for analyzing SR-based issues [48][49][50][51].…”

Section: • Symbolic Regression (Sr)mentioning

confidence: 99%

Short-Term Forecasting of Water Yield from Forested Catchments after Bushfire: A Case Study from Southeast Australia

Gharun

Azmi

Adams

2015

Water

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Forested catchments in southeast Australia play an important role in supplying water to major cities. Over the past decades, vegetation cover in this area has been affected by major bushfires that in return influence water yield. This study tests methods for forecasting water yield after bushfire, in a forested catchment in southeast Australia. Precipitation and remotely sensed Normalized Difference Vegetation Index (NDVI) were selected as the main predictor variables. Cross-correlation results show that water yield with time lag equal to 1 can be used as an additional predictor variable.

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The Analysis of the Flash Flood Produced During 02–04.06.2016 in the Trotuș River Basin (Romania)

Manolache¹

2018

Present Environment and Sustainable Development

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This paper concentrates information on the flash flood produced during 02-04.06.2016 in the Trotuş River basin. Maximum flows have been analyzed by reference to the thresholds corresponding to the Defense Levels. In order to show in a suggestive way the amplitude of the event, the characteristics of the flood were highlighted as well as a series of morphometric and hydrological data on the maximum flow of the Trotuș River. The hydrometric stations in the basin area, to which reference was made, were: Lunca de Sus, Goioasa, Tg. Ocna, Onești and Vrânceni. The flow series were represented graphically to be able to easily observe the evolution of the flood over time. The analysis showed that the flood produced on the Trotuș River had a great magnitude causing significant damages.

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Simplified approach for flood estimation and propagation

Cited by 5 publications

References 5 publications

A data fusion‐based drought index

A data fusion‐based drought index

Short-Term Forecasting of Water Yield from Forested Catchments after Bushfire: A Case Study from Southeast Australia

The Analysis of the Flash Flood Produced During 02–04.06.2016 in the Trotuș River Basin (Romania)

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