Regional modelling of rainfall-induced runoff using hydrological model by incorporating plant cover effects: case study in Kelantan,Malaysia

The objective of this paper is to develop a spatial temporal runoff modelling of local rainfall patterns effect on the plant cover hilly lands in Kelantan River Basin. Rainfall interception loss based on leaf area index, loss/infiltration on the ground surface, and runoff calculation were considered as the main plant cover effects on the runoff volume. In this regard, a hydrological and geotechnical grid-based regional model (integrated model) was performed using Microsoft Excel^® and GIS framework system for deterministic modelling of rainfall-induced runoff by incorporating plant cover effects. The infiltration process of the current model was integrated with the precipitation distribution method and rainfall interception approach while the runoff analysis of integrated model was employed based on loss/infiltration water on the ground surface with consideration of water interception loss by canopy and the remaining surface water. In the following, the spatial temporal analysis of rainfall-induced runoff was performed using 10 days of hourly rainfall events at the end of December 2014 in Kelantan River Basin. The corresponding changes in pressure head and consequent rate of infiltration were calculated during rainfall events. Subsequently, flood volume is computed using local rainfall patterns, along with water interception loss and the remaining surface water in the study area. The results showed the land cover changes caused significant differences in hydrological response to surface water. The increase in runoff volume of the Kelantan River Basin is as a function of deforestation and urbanization, especially converting the forest area to agricultural land (i.e. rubber and mixed agriculture).     

Logistic regression model for sinkhole susceptibility due to damaged sewer pipes

The occurrence of anthropogenic sinkholes in urban areas can lead to severe socioeconomic losses. A damaged underground sewer pipe is regarded as one of the primary causes of such a phenomenon. This study adopted the best subsets regression method to produce a logistic regression model that evaluates the susceptibility for sinkholes induced by damaged sewer pipes. The model was developed by analyzing the sewer pipe network as well as cases of sinkholes in Seoul, South Korea. Among numerous sewer pipe characteristics tested as explanatory variables, the length, age, elevation, burial depth, size, slope, and materials of the sewer pipe were found to influence the occurrence of sinkhole. The proposed model reasonably estimated the sinkhole susceptibility in the area studied, with an area value under the receiver-operating characteristics curve of 0.753. The proposed methodology will serve as a useful tool that can help local governments to choose a cavity inspection regime, and to prevent sinkholes induced by damaged sewer pipes.     

Volume estimation and stage division of the Mahu landslide in Sichuan Province,China

The Mahu lake, the third deepest lake in China, is located on the west bank of the Jinsha River in Leibo county, Sichuan Province. It is a dammed lake created by an old landslide on the ancient Huanglang river, a tributary on the west bank of the Jinsha River. Previous studies (Wang and Lu in J Mt Res S1:44–47, 2000) suggested that this landslide was caused by an earthquake approximately 372 ka (Middle Pleistocene), during which a few hundreds of million cubic meters of debris were deposited between 1177 and 900 m a.s.l. (above sea level), covering an area of around 15 km². Our further investigations, including geodetic survey, borehole drilling, and field reconnaissance, combining with five chronological data, have made some new discoveries at this site. First, the toe of the landslide extends from 900 m a.s.l. down to 320 m a.s.l., i.e., the local bed elevation of the contemporary Jinsha River. Second, the area of the landslide deposits is 17.3 km² with a volume of 2.38 km³, much larger than the previous estimation. Thus, it should be one of the largest known landslides in China. And the lower elevation of the landslide’s toe also rules out the possibility that it is a hanging valley on the ancient Huanglang river. Our work suggests that this landslide was created by five events according to the overlapping characteristics of the deposits and five chronological data, which are old than 52,600 years, old than 16,000 years, old than 15,500 years, 5800 years, and old than 4200 years, respectively.     

Remote sensing-based assessment of vegetation damage by a strong typhoon (Meranti) in Xiamen Island,China

Remote sensing is a cost-effective tool for assessing vegetation damage by typhoon events at various scales. Taking Xiamen Island, southeastern China, as a study case, this paper aimed to assess and analyze the vegetation damage caused by Typhoon Meranti landfalling on September 15, 2016, using two high spatial resolution remote sensing images before and after the typhoon event. Seven severely damaged vegetation regions were selected based on the classification of vegetation types and visual interpretation of the images. Regression analysis was used to correct seasonal variation of the two high-solution images before and after typhoon. The vegetation area of the whole of Xiamen Island and the selected seven regions before and after typhoon were then calculated, respectively. Two spectral vegetation indicators, normalized difference vegetation index (NDVI) and fractional vegetation coverage (FVC), were also retrieved for the whole island and the seven regions. By comparing the difference in NDVI values before and after the typhoon of the two high spatial resolution images, we analyzed the most affected vegetation areas, as well as the most seriously damaged vegetation species. The typhoon has caused a decrease in vegetation area by 95.1 ha across the whole Xiamen Island. The mean NDVI and FVC decreased by 0.209 and 13 percentage points, respectively. While, in the seven selected severely damaged areas, the mean NDVI decreased by 0.356–0.444 and FVC decreased by 27–42 percentage points. The visual inspection showed that the tone of typhoon-damaged vegetation became darker, the patches of damaged vegetation became smaller and more fragmented, and the gap between vegetation canopies became larger. The most affected vegetation areas occurred in the southeastern hilly area, Jinshang and Hubin South Roads, as well as the Wuyuan Bay area. The most seriously damaged vegetation type is broad-leaved trees, especially the species, Acacia confusa, Delonix regia, Bauhinia variegata, Chorisia speciosa, Ficus benjamina and F. Concinna.     

Participatory Vulnerability Reduction (PVR): an urban community-based approach for earthquake management

Participation of local people during any disaster is enormous. They possess better knowledge and information about their own community than anyone else from the outside. This study proposes Participatory Vulnerability Reduction (PVR), a community-based approach for disaster management. The concept of PVR was applied to an urban community of Dhaka city (Ward no. 06 of Dhaka North City Corporation) which has been identified by the Comprehensive Disaster Management Programme as one of the most vulnerable areas of the city for earthquake. PVR consists three steps, and in each step, different participatory urban appraisal tools were used. In the first step, the community people assessed the earthquake vulnerability. It was found that some certain parts of the study area are highly vulnerable due to lack of accessibility to the critical facilities, inadequacy of open space, poor construction practice and unsuitable soil condition for building construction. This was followed by analyzing the root causes and effects of these problems. Structural fragility of the buildings, construction of settlements by filling the low-lying areas and development of slums beside taller buildings are the three major causes behind the above vulnerable issues. In the second step, capacity of the community was assessed in terms of resources and their organizational structure. In the final step, local people developed the strategies to overcome the vulnerability and a community-based organizational set up was proposed to coordinate the collective actions. Although developed in local context, application of PVR is not limited for earthquake and it can be replicated for other communities as well.     

Examination of the structural characteristics arising in gypsums by the GPR and MASW methods (Sivas,Turkey)

This study was performed at an area of 50?×?48 m² being defined as a new settlement in the northeast of Sivas. In the study, the discontinuities that are not deep and their geophysical characteristics were examined by the GPR and MASW methods. For interpretation, GPR cross sections were prepared as 2D–3D, and MASW cross sections were prepared as 2D. As for geophysical cross sections, about 10 m depth was examined. It was understood that the reflections observed in the form of hyperbolas in GPR cross sections correspond to areas having low S wave velocity (V_s) in MASW cross sections. It was understood that the S wave velocities are lower than 653 m/s, that the seismic velocities in between 653 and 275 m/s indicate partially deteriorated areas and that the S wave velocities of unweathered gypsums are higher than 1275 m/s at these low-velocity zones. Thus, it was thought that the fill material that may arise in the fracture, crack and deterioration areas arises from intercalation and clastic gypsum units, and that it plays a role in having low value S wave velocities. In all the geophysical cross sections, it was understood that the structures with gypsum are intense at the initial 5 m. And a fracture at the south of the study area, that it was estimated might be longer than 40 m, was determined as the largest gypsum structure. It was understood that this fracture starts from a depth of about 5 m in the west and that it slopes down to 7 m depth in the east. According to these results, it was understood that the damage amount arising in time in the gypsum structures from the effect of water may increase, the study area was defined as risky, and the required importance should be attached to these structures especially in foundation engineering.     

Integration of GIS,AHP and TOPSIS for earthquake hazard analysis

Worldwide, earthquakes and related disasters have persistently had severe negative impacts on human livelihoods and have caused widespread socioeconomic and environmental damage. The severity of these disasters has prompted recognition of the need for comprehensive and effective disaster and emergency management (DEM) efforts, which are required to plan, respond to and develop risk mitigation strategies. In this regard, recently developed methods, known as multi-criteria decision analysis (MCDA), have been widely used in DEM domains by emergency managers to greatly improve the quality of the decision-making process, making it more participatory, explicit, rational and efficient. In this study, MCDA techniques of the Analytical Hierarchical Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), integrated with GIS, were used to produce earthquake hazard and risk maps for earthquake disaster monitoring and analysis for a case study region of Küçükçekmece in Istanbul, Turkey. The five main criteria that have the strongest influence on the impact of earthquakes on the study region were determined: topography, distance to epicentre, soil classification, liquefaction and fault/focal mechanism. AHP was used to determine the weights of these parameters, which were also used as input into the TOPSIS method and GIS (ESRI ArcGIS) for simulating these outputs to produce earthquake hazard maps. The resulting earthquake hazard maps created by both the AHP and TOPSIS models were compared, showing high correlation and compatibility. To estimate the elements at risk, population and building data were used with the AHP and TOPSIS hazard maps for potential loss assessment; thus, we demonstrated the potential of integrating GIS with AHP and TOPSIS in generating hazard maps for effective earthquake disaster and risk management.     

Evaluating climate change adaptation efforts on the US 50 states’ hazard mitigation plans

Climate change brings uncertain risks of climate-related natural hazards. The US Federal Emergency Management Agency (FEMA in Climate change: long-term trends and their implications for emergency management, 2011. https://www.fema.gov/pdf/about/programs/oppa/climate_change_paper.pdf) has issued a policy directive to integrate climate change adaptation actions into hazard mitigation programs, policies, and plans. However, to date there has been no comprehensive empirical study to examine the extent to which climate change issues are integrated into state hazard mitigation plans (SHMPs). This study develops 18 indicators to examine the extent of climate change considerations in the 50 SHMPs. The results demonstrate that these SHMPs treat climate change issues in an uneven fashion, with large variations present among the 50 states. The overall plan quality for climate change considerations was sustained at an intermediate level with regard to climate change-related awareness, analysis, and actions. The findings confirm that climate change concepts and historic extreme events have been well recognized by the majority of SHMPs. Even though they are not specific to climate change, mitigation and adaptation strategies that can help reduce climate change risks have been adopted in these plans. However, the plans still lack a detailed assessment of climate change and more incentives for collaboration strategies beyond working with emergency management agencies.     

Settlement of Shallow Foundations on Cohesionless Soils Based on SPT Value Using Multi-Objective Feature Selection

Accurate prediction of settlement for shallow footings on cohesionless soil is a complex geotechnical problem due to large uncertainties associated with soil. Prediction of the settlement of shallow footings on cohesionless soil is based on in situ tests as it is difficult to find out the properties of soil in the laboratory and standard penetration test (SPT) is the most often used in situ test. In data driven modelling, it is very difficult to choose the optimal input parameters, which will govern the model efficiency along with a better generalization. Feature subset selection involves minimization of both prediction error and the number of features, which are in general mutual conflicting objectives. In this study, a multi-objective optimization technique is used, where a non-dominated sorting genetic algorithm (NSGA II) is combined with a learning algorithm (neural network) to develop a prediction model based on SPT data based on the Pareto optimal front. Pareto optimal front gives the user freedom to choose a model in terms of accuracy and model complexity. It is also shown how NSGA II can be effectively applied to select the optimal parameters and besides minimizing the error rate. The developed model is compared with existing models in terms of different statistical criteria and found to be more efficient.