Developing a cloud-based toolbox for sensitivity analysis of a water quality model. (July 2021)

• Record Type: Journal Article
• Authors: Kim, Soobin; Kwon, Yong Sung; Pyo, JongCheol; Ligaray, Mayzonee; Min, Joong-Hyuk; Ahn, Jung Min; Baek, Sang-Soo; Cho, Kyung Hwa
• Is Part Of: Environmental modelling & software. Volume 141(2021)
• Publication Date: 2021-07
• Subjects: Cloud-based toolbox -- Cloud computing system -- Water quality model -- Sensitivity analysis -- Hyperspectral imaging data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105068
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The complexity associated with water quality models (WQMs) has increased owing to the introduction of numerous physical and biological mechanisms in the models. Sensitivity analysis (SA) is conducted to identify influential parameters in these mechanisms. However, enormous computational power and time are required to obtain numerical solutions from thousands of model simulations. Therefore, a cloud-based toolbox is developed for performing SA of WQMs by implementing a cloud computing system using grab sampling data and hyperspectral images (HSI) of waterbodies. Cloud computing can provide high-performance computation by adjusting the scale of the computational power according to user preference. The developed toolbox with the cloud system can reduce the computation time for SA by approximately 20 times compared to that of a desktop computer. Highlights: A cloud-based toolbox for model sensitivity analysis was developed. The cloud-based toolbox can reduce the computation time by approximately 20 times. The sensitivity of parameters was different depending on HSI and grab sampling data. Our toolbox provides a solution for the computational burden of water quality model.