Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach

Alexander, AA and Kumar, DN (2024) Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach. In: Advances in Water Resources, 194 .

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Abstract

Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modelers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach. © 2024 Elsevier Ltd

Item Type:	Journal Article
Publication:	Advances in Water Resources
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to the publisher.
Keywords:	Convolutional neural network; Dynamically dimensioned search; Hydrological models; Model calibration; Multiple-modeling; Optimal parameter; Optimizing parameters; Parameters estimation; Physics-based models; Structure for unifying multiple modeling alternative, Neural network models, algorithm; artificial neural network; calibration; hydrological modeling; optimization; parameter estimation
Department/Centre:	Division of Mechanical Sciences > Divecha Centre for Climate Change Division of Mechanical Sciences > Civil Engineering
Date Deposited:	26 Nov 2024 10:31
Last Modified:	26 Nov 2024 10:31
URI:	http://eprints.iisc.ac.in/id/eprint/86888

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