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Advancing model calibration and uncertainty analysis of SWAT models using cloud computing infrastructure: LCC-SWAT
Journal of Hydroinformatics ( IF 2.7 ) Pub Date : 2021-01-01 , DOI: 10.2166/hydro.2020.066
Masood Zamani 1, 2 , Narayan Kumar Shrestha 1 , Taimoor Akhtar 1 , Trevor Boston 3 , Prasad Daggupati 1
Affiliation  

Calibration and uncertainty analysis of a complex, over-parameterized environmental model such as the Soil and Water Assessment Tool (SWAT) requires thousands of simulation runs and multiple calibration iterations. A parallel calibration system is thus desired that can be deployed on cloud-based architectures for reducing calibration runtime. This paper presents a cloud-based calibration and uncertainty analysis system called LCC-SWAT that is designed for SWAT models. Two optimization techniques, sequential uncertainty fitting (SUFI-2) and dynamically dimensioned search (DDS), have been implemented in LCC-SWAT. Moreover, the cloud-based system has been deployed on the Southern Ontario Smart Computing Innovation Platform's (SOSCIP) Cloud Analytics platform for diagnostic assessment of parallel calibration runtime on both single-node and multi-node CPU architectures. Unlike other calibrations/uncertainty analysis systems developed on the cloud, this system is capable of generating a comprehensive set of statistical information automatically, which facilitates broader analyses of the performance of the SWAT models. Experimental results on SWAT models of different complexities showed that LCC-SWAT can reduce runtime significantly. The runtime reduction is more pronounced for more complex and computationally intensive models. However, the reported runtime efficiency is significantly higher for single node systems. Comparative experiments with DDS and SUFI-2 show that parallel DDS outperforms parallel SUFI-2 in terms of both parameter identifiability and reducing uncertainty in model simulations. LCC-SWAT is a flexible calibration system and other optimization algorithms and asynchronous parallelization strategies can be added to it in future.



中文翻译:

使用云计算基础架构推进SWAT模型的模型校准和不确定性分析:LCC-SWAT

对复杂的,超参数化的环境模型(例如土壤和水评估工具(SWAT))进行校准和不确定性分析需要数千次模拟运行和多次校准迭代。因此,期望可以在基于云的架构上部署并行校准系统以减少校准时间。本文介绍了一种专为SWAT模型设计的基于云的校准和不确定性分析系统LCC-SWAT。LCC-SWAT已实现了两种优化技术,即顺序不确定性拟合(SUFI-2)和动态尺寸搜索(DDS)。此外,基于云的系统已部署在安大略省南部智能计算创新平台上 的(SOSCIP)Cloud Analytics平台,用于对单节点和多节点CPU架构上的并行校准运行时进行诊断评估。与在云上开发的其他校准/不确定性分析系统不同,该系统能够自动生成一组全面的统计信息,从而有助于更广泛地分析SWAT模型的性能。在不同复杂度的SWAT模型上的实验结果表明,LCC-SWAT可以显着减少运行时间。对于更复杂且计算量大的模型,运行时间的减少更为明显。但是,对于单节点系统,报告的运行时效率明显更高。与DDS和SUFI-2进行的比较实验表明,在参数可识别性和减少模型仿真的不确定性方面,并行DDS优于并行SUFI-2。LCC-SWAT是一种灵活的校准系统,将来可以向其添加其他优化算法和异步并行化策略。

更新日期:2021-01-22
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