| 1. |
- Luu, Chinh, et al.
(författare)
-
Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam
- 2020
-
Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7, s. 1-17
-
Tidskriftsartikel (refereegranskat)abstract
- Vietnam has been extensively affected by floods, suffering heavy losses in human life andproperty. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high- resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of QuangBinh province is a valuable tool and source for flood preparedness activities at the local scale.
|
|
| 2. |
- Ngo, Son Tung, et al.
(författare)
-
Distal Hydrophobic Loop Modulates the Copper Active Site and Reaction of AA13 Polysaccharide Monooxygenases
- 2022
-
Ingår i: Journal of Physical Chemistry B. - : American Chemical Society (ACS). - 1520-6106 .- 1520-5207. ; 126:39, s. 7567-7578
-
Tidskriftsartikel (refereegranskat)abstract
- Polysaccharide monooxygenases (PMOs) use a type-2 copper center to activate O2 for the selective hydroxylation of one of the two C-H bonds of glycosidic linkages. Our electron paramagnetic resonance (EPR) analysis and molecular dynamics (MD) simulations suggest the unprecedented dynamic roles of the loop containing the residue G89 (G89 loop) on the active site structure and reaction cycle of starch-active PMOs (AA13 PMOs). In the Cu(II) state, the G89 loop could switch between an open and closed conformation, which is associated with the binding and dissociation of an aqueous ligand in the distal site, respectively. The conformation of the G89 loop influences the positioning of the copper center on the preferred substrate of AA13 PMOs. The dissociation of the distal ligand results in the bending of the T-shaped core of the Cu(II) active site, which could help facilitate its reduction to the active Cu(I) state. In the Cu(I) state, the G89 loop is in the closed conformation with a confined copper center, which could allow for efficient O2 binding. In addition, the G89 loop remains in the closed conformation in the Cu(II)-superoxo intermediate, which could prevent off-pathway superoxide release via exchange with the distal aqueous ligand. Finally, at the end of the reaction cycle, aqueous ligand binding to the distal site could switch the G89 loop to the open conformation and facilitate product release.
|
|
| 3. |
- Nhu, Viet-Ha, et al.
(författare)
-
Shallow Landslide Susceptibility Mapping : A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
- 2020
-
Ingår i: International Journal of Environmental Research and Public Health. - Switzerland : MDPI. - 1661-7827 .- 1660-4601. ; 17:8, s. 1-30
-
Tidskriftsartikel (refereegranskat)abstract
- Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
|
|
