Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia
Article
Article Title | Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia |
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ERA Journal ID | 3551 |
Article Category | Article |
Authors | Rahmati, Omid (Author), Panahi, Mahdi (Author), Kalantari, Zahra (Author), Soltani, Elinaz (Author), Falah, Fatemeh (Author), Dayal, Kavina S. (Author), Mohammadi, Farnoush (Author), Deo, Ravinesh C. (Author), Tiefenbacher, John (Author) and Bui, Dieu Tien (Author) |
Journal Title | Science of the Total Environment |
Journal Citation | 718, pp. 1-17 |
Article Number | 134656 |
Number of Pages | 17 |
Year | 2020 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0048-9697 |
1879-1026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.scitotenv.2019.134656 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0048969719346479 |
Abstract | Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans. |
Keywords | drought hazard; spatial modeling; hybrid model; GIS; Australia |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ton Duc Thang University, Vietnam |
Kangwon National University, Korea | |
Stockholm University, Sweden | |
Shiraz University, Iran | |
Lorestan University, Iran | |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
University of Tehran, Iran | |
School of Sciences | |
Texas State University, United States | |
Duy Tan University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5814/capability-and-robustness-of-novel-hybridized-models-used-for-drought-hazard-modeling-in-southeast-queensland-australia
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