| 1. |
- Lei, Xinxiang, et al.
(author)
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Urban flood modeling using deep-learning approaches in Seoul, South Korea
- 2021
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In: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 601
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Journal article (peer-reviewed)abstract
- Identification of flood-prone sites in urban environments is necessary, but there is insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping in urban areas. This study evaluated the capability of convolutional neural network (NNETC) and recurrent neural network (NNETR) models for flood hazard mapping. A flood-inundation inventory (including 295 flooded sites) was used as the response variable and 10 flood-affecting factors were considered as the predictor variables. Flooded sites were then spatially randomly split in a 70:30 ratio for building flood models and for validation purposes. The prediction quality of the models was validated using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The validation results indicated that prediction performance of the NNETC model (AUC = 84%, RMSE = 0.163) was slightly better than that of the NNETR model (AUC = 82%, RMSE = 0.186). Both models indicated that terrain ruggedness index was the most important predictor, followed by slope and elevation. Although the model output had a relative error of up to 20% (based on AUC), this modeling approach could still be used as a reliable and rapid tool to generate a flood hazard map for urban areas, provided that a flood inundation inventory is available.
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| 2. |
- Kazemi, Fatemeh, et al.
(author)
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The interaction and synergic effect of particle size on flotation efficiency: A comparison study of recovery by size, and by liberation between lab and industrial scale data : [Sinergijski utjecaj djelovanja veličine zrna na učinkovitost flotacije: usporedba laboratorijskih i industrijskih podataka o iskorištenju korisne komponente u koncentratu ovisno o veličini zrna i raščinu (stupnju oslobođenja)]
- 2023
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In: Rudarsko-Geološko-Naftni Zbornik. - : Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb. - 0353-4529 .- 1849-0409. ; 38:1, s. 1-12
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Journal article (peer-reviewed)abstract
- The interaction and synergic effect of particle size on flotation efficiency were investigated by a comparison study between laboratories (size-by-size flotation modes) and industrial scale operational data (whole mixed size fraction). For this purpose, sampling was done from the feed, concentrate, and tailing of the flotation rougher cells of the Sungun copper processing complex (located in the northwest of Iran). In the size-by-size flotation mode (lab scale), the sample was first subjected to different size fractions, and then flotation tests were performed for each fraction. On an industrial scale, the particle size distribution of feed, concentrate, and tailing of flotation of the rougher stage have been analyzed. According to the results, in the case of industrial flotation mode (whole mixed size fraction), the particles with d80=84 μm were more likely to reach the tailing of flotation, and the particles within the size range of +63-180 μm constituted the highest amount of concentrate particles. In lab flotation mode (size-by-size), the maximum recovery was in the size fraction of +40-60 μm. By comparing the two flotation modes of industrial (whole mixed size fraction) and lab (size-by-size), for fractions <45 μm, the industrial flotation recovery was approximately 40% greater than the lab flotation recovery. However, for fractions >125 μm, the recovery trend was reversed and the lab flotation recovery was greater than the industrial flotation recovery. Coarse particle flotation has significant economic and technological benefits. By improving the recovery of coarse particles during the flotation process, the amount of grinding requirements will be reduced and consequently, it will considerably decrease the amount of energy consumption.
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| 3. |
- Nhu, Viet-Ha, et al.
(author)
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Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models
- 2020
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In: Water. - Switzerland : MDPI. - 2073-4441. ; 12:4
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Journal article (peer-reviewed)abstract
- Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales.
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| 4. |
- Rahmati, Omid, et al.
(author)
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Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia
- 2020
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In: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 718
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Journal article (peer-reviewed)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 (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.
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