A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India

Shekar, Padala Raja; Mathew, Aneesh; Yeswanth, P.V.; Deivalakshmi, S.

doi:10.1016/j.aiig.2024.100073

Cited by 5 publications

(1 citation statement)

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“…Furthermore, runoff routing models lack the tools needed to carry out comprehensive uncertainty analysis under changing climate [118][119][120][121]. Artificial intelligence-based methods can also be incorporated with the runoff routing models to enhance the uncertainty analysis due to data limitation [112][113][114][115][116][117][118][119][120][121][122][123][124].…”

Section: Current Situation and Future Trendsmentioning

confidence: 99%

A Review of Event-Based Conceptual Rainfall-Runoff Models: A Case for Australia

Ali,

Rahman,

Shaik

2024

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Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments.

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