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Capability and robu...
Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia
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Rahmati, Omid (author)
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Panahi, Mahdi (author)
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- Kalantari, Zahra (author)
- Stockholms universitet,Institutionen för naturgeografi
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Soltani, Elinaz (author)
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Falah, Fatemeh (author)
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Dayal, Kavina S. (author)
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Mohammadi, Farnoush (author)
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Deo, Ravinesh C. (author)
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Tiefenbacher, John (author)
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Dieu, Tien (author)
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(creator_code:org_t)
- Elsevier BV, 2020
- 2020
- English.
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In: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 718
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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- 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 (AUC(mean) = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUC(mean) = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUC(mean) = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUC(mean) = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUC(mean) = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plantavailable 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
Keyword
- Drought hazard
- Spatial modeling
- Hybrid model
- GIS
- Australia
Publication and Content Type
- ref (subject category)
- art (subject category)
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- By the author/editor
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Rahmati, Omid
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Panahi, Mahdi
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Kalantari, Zahra
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Soltani, Elinaz
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Falah, Fatemeh
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Dayal, Kavina S.
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show more...
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Mohammadi, Farno ...
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Deo, Ravinesh C.
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Tiefenbacher, Jo ...
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Dieu, Tien
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show less...
- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Earth and Relate ...
- Articles in the publication
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Science of the T ...
- By the university
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Stockholm University
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Royal Institute of Technology