| 1. |
- Andersson, Erik, et al.
(författare)
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Ambio fit for the 2020s
- 2022
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Ingår i: Ambio. - : Springer Nature. - 0044-7447 .- 1654-7209. ; 51:5, s. 1091-1093
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Rahmati, O., et al.
(författare)
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Development of novel hybridized models for urban flood susceptibility mapping
- 2020
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.
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| 3. |
- Blösch, Günter, et al.
(författare)
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Twenty-three unsolved problems in hydrology (UPH) - a community perspective
- 2019
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 64:10, s. 1141-1158
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Tidskriftsartikel (refereegranskat)abstract
- This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.
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| 4. |
- Darabi, H., et al.
(författare)
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Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood
- 2021
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Ingår i: Geocarto International. - : Taylor and Francis Ltd.. - 1010-6049 .- 1752-0762.
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Tidskriftsartikel (refereegranskat)abstract
- In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.
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| 5. |
- Sohrabi, N., et al.
(författare)
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A probabilistic-deterministic analysis of human health risk related to the exposure to potentially toxic elements in groundwater of Urmia coastal aquifer (NW of Iran) with a special focus on arsenic speciation and temporal variation
- 2020
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Ingår i: Stochastic environmental research and risk assessment (Print). - : Springer Science and Business Media Deutschland GmbH. - 1436-3240 .- 1436-3259.
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Tidskriftsartikel (refereegranskat)abstract
- The human exposure to groundwater contamination with toxic elements is a worldwide concern. In this study, multivariate statistics coupled with probabilistic and deterministic risk estimation approaches were applied to 173 groundwater samples of Urmia aquifer (UA) to evaluate human health risks in relation to the consumption of groundwater contaminated with toxic elements. The concentrations of aluminum (Al), barium (Ba), cadmium (Cd), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn) were below their corresponding maximum permissible levels as advised by the WHO, USEPA, and Iranian guidelines. However, arsenic (As), lead (Pb), iron (Fe), and selenium (Se) were elevated at some locations. Monte Carlo simulation-based probabilistic risk estimation suggested ingestion as the dominant pathway for water-hosted element exposure. Mean values of hazard index estimated for As exposure from combined ingestion and dermal contact pathways exceeded the safe level of 1.0 for both adults and children, indicated potential non-carcinogenic health risks. The total cancer risk induced by groundwater As exceeded the acceptable limit of 1 × 10–4. Sensitivity analysis highlighted exposure duration, element concentration in water, and average time as the most significant variables causing the probable health risks. Speciation modeling using PHREEQC highlighted the occurrence of As(V) and As(III) in groundwater of the UA. Reductive dissolution of Fe(III) (oxyhydr)oxides and clay minerals was identified as the main controlling mechanism of As mobilization. This communication emphasizes the need for appropriate approaches in mitigating toxic element contamination of water resources in coastal parts of the UA to safeguard public health from carcinogenic and non-carcinogenic risks.
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