Integration of Adaptive Emulators and Sensitivity Analysis for Enhancement of Complex Hydrological Models
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Integration of Adaptive Emulators and Sensitivity Analysis for Enhancement of Complex Hydrological Models Venkatesh Budamala 1,2 & Amit Baburao Mahindrakar 1 Received: 24 May 2020 / Accepted: 16 September 2020/ # Springer Nature Switzerland AG 2020
Abstract
A hydrological system involves a significant number of parameters to describe the realworld phenomena which ultimately reflects into the computational burden during model fitting and simulation. In this study, the proposed Adaptive Emulator Modelling based Optimization (AEMO) framework is presented which minimizes the complexity and computational burden, and enhances the overall efficiency of the representation of a physical watershed model. Here, AEMO consists of machine learning, sensitivity analysis, adaptive modelling, and sampling. The efficacy of the proposed AEMO framework is carried out through the Soil and Water Assessment Tool (SWAT) hydrological model for Peachtree Creek Watershed, Atlanta, USA. This watershed is subjected to flash floods in case of heavy rains due to a number of narrow streams situated in urban areas, and the water levels can rise quickly within a few hours or minutes of a rainfall event. Hence, this type of watershed is more complicated during the simulation and it is necessary to analyze the sensitivity during calibration of each iteration to provide information for the next iteration. The results concluded that the AEMO achieved exceptional results in both performance and computational burden when compared to the existing method. The NSE performance metric for the default SWAT model and hybrid model (SWAT + AEMO) showed the values 0.49 and 0.81, respectively. Hence, it displayed that AEMO enhanced the SWAT conceptual model prediction. The proposed framework incorporating future climate data can provide accurate information on water and disaster management. Keywords Adaptive Model . Optimization . Sensitivity Analysis . SWAT Modeling . Hydrological Processes . Urbanization
* Amit Baburao Mahindrakar [email protected]; [email protected]
1
Department of Environmental and Water Resources Engineering, School of Civil Engineering, Vellore Institute of Technology, Tamil NaduVellore 632014, India
2
Centre for Disaster Mitigation and Management, Vellore Institute of Technology, 632014 Vellore, Tamil Nadu, India
V. Budamala, A. Baburao Mahindrakar
1 Introduction In recent years, computer-based simulation models in hydrology have become an essential tool for planning and management of hydrological flows and generally water resources. These simulation models can be simple or complex, depending upon the scale or number of parameters or mathematical formulation, and hence, will lead to low or high computational burden (Pecci et al. 2019). The computational burden of simulation models is directly proportional to the: (i) selection of relevant model parameters related to the physical processes; and (ii) estimation of the selected parameters for narrowing factor space (Gong and Duan 2017). Further, the computational burden depe
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