An interactive Web-GIS fluvial flood forecast and alert system in operation in Portugal

Mourato, Sandra; Fernandez, Paulo; Marques, Fábio; Pereira, L. Gomes

doi:10.1016/j.ijdrr.2021.102201

Cited by 33 publications

(15 citation statements)

References 86 publications

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“…(1) SA Density-based measure We use the physical and hydrological parameters for the sub-basins obtained from HEC-GeoHMS and available in previous literature (Ramly and Tahir 2016;Ramly et al 2020;Mourato et al 2021). As mentioned in Sect.…”

Section: Hec-hms Model Implementationmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Stoch Environ Res Risk Assess

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Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge of physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to distinguish in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.

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Section: Hec-hms Model Implementationmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…We use the physical and hydrological parameters for the sub-basins obtained from HEC-GeoHMS and available in previous literature (Ramly and Tahir, 2016;Ramly et al, 2020;Mourato et al, 2021). As mentioned in Section 3.1, rainfall data are collected from three rain gauge stations (see panel c in Figure 1).…”

Section: Hec-hms Model Implementationmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Preprint

View full text Add to dashboard Cite

Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge on physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to discriminate in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.

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“…Usually, ood prediction approaches can be divided into two categories, physically based models, and data-driven models. Physical models (Mourato et al, 2021;Pierini et al, 2014) often require substantial amount of both hydrological and geomorphological data for calibration and validation, and they might not always be readily accessible. Furthermore, the model parameters must be carefully tested and evaluated, because they are regionally dependent and can be challenging to estimate.…”

Section: Introductionmentioning

confidence: 99%

Behavior of LSTM and Transformer Deep Learning Models in Flood Simulation Considering South Asian Tropical Climate

Madhushanka,

Jayasinghe,

Rajapakse

2024

Preprint

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The imperative for a reliable and accurate flood forecasting procedure stem from the hazardous nature of the disaster. In response, researchers are increasingly turning to innovative approaches, particularly machine learning models, which offer enhanced accuracy compared to traditional methods. However, a notable gap exists in the literature concerning studies focused on the South Asian tropical region, which possesses distinct climate characteristics. This study investigates the applicability and behavior of Long Short-Term Memory (LSTM) and Transformer models in flood simulation with one day lead time, at the lower reach of Mahaweli catchment in Sri Lanka, which is mostly affected by the Northeast Monsoon. The importance of different input variables in the prediction was also a key focus of this study. Input features for the models included observed rainfall data collected from three nearby rain gauges, as well as historical discharge data from the target river gauge. Results showed that use of past water level data denotes a higher impact on the output compared to the other input features such as rainfall, for both architectures. All models denoted satisfactory performances in simulating daily water levels, especially low stream flows, with Nash Sutcliffe Efficiency (NSE) values greater than 0.77 while Transformer Encoder model showed a superior performance compared to Encoder Decoder models.

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An interactive Web-GIS fluvial flood forecast and alert system in operation in Portugal

Cited by 33 publications

References 86 publications

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Behavior of LSTM and Transformer Deep Learning Models in Flood Simulation Considering South Asian Tropical Climate

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