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

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dc.contributor.authorShekar, Padala Raja
dc.contributor.authorMathew, Aneesh
dc.contributor.authorYeswanth, P.V.
dc.contributor.authorDeivalakshmi, S
dc.date.accessioned2026-05-11T07:24:19Z
dc.date.available2026-05-11T07:24:19Z
dc.date.issued2024-02-11
dc.description.abstractIn recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed, India. These models included the artificial neural network (ANN), k-nearest neighbour regression model (KNN), extreme gradient boosting (XGBoost) regression model, random forest regression model (RF), convolutional neural network (CNN), and CNN-RNN (convolutional recurrent neural network). The years 2003–2007 are classified as the calibration or training period, while the years 2008–2009 are classified as the validation or testing period for the span of time 2003 to 2009. The available rainfall, maximum and minimum temperatures, and discharge data were collected and utilized in the models. To compare the performance of the models, five criteria were employed: R2, NSE, MAE, RMSE, and PBIAS. The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods (training: R2 is 0.99, NSE is 0.99, MAE is 1.76, RMSE is 3.11, and PBIAS is −1.45; testing: R2 is 0.97, NSE is 0.97, MAE is 2.05, RMSE is 3.60, and PBIAS is −3.94). These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study. The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management, flood control, and environmental planning.
dc.identifier.issn2666-5441
dc.identifier.urihttps://doi.org/10.1016/j.aiig.2024.100073
dc.identifier.urihttp://nitt.ndl.gov.in/handle/123456789/162
dc.language.isoen
dc.publisherKeAi Communications Co. Ltd.
dc.relation.ispartofseriesArtificial Intelligence in Geosciences; Vol. 5 / No. 100073
dc.subjectTECHNOLOGY::Other technology::Environmental engineering
dc.subjectNATURAL SCIENCES::Earth sciences::Atmosphere and hydrosphere sciences::Hydrology
dc.subjectTECHNOLOGY::Civil engineering and architecture::Water engineering
dc.titleA combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India
dc.typeArticle
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