Browsing by Author "Kalpana, LDCHN"

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    An Alternative Approach to Assess The Residential Population Resilience to Urban Flooding
    (Faculty of Architecture University of Moratuwa, 2020-11) Kalpana, LDCHN; Jayasinghe, AB; Abenayake, CC; Wijayawardana, PNP
    Community resilience assessments and minimizing the anticipated disruptions to vulnerable communities, is a broad topic in disaster studies. In common practice, most of the indicator-based resilience assessment studies rely on statistical aggregation methods of tabular data collected for macro administrative units, as it is readily available in most of the countries. However, this method confronts severe drawbacks in converting such data into micro-scale geospatial units. To address those issues, this study proposes to utilize the Dasymetric Mapping Technique in the geospatial population resilience assessments, as it is capable of identifying the micro level impact to the population distribution as a pixel representation. In order to geospatially demonstrate the population exposure, the study has selected three major flooding events occurred in Colombo, Sri Lanka. The results revealed a great applicability of the proposed method as a statistical approach which estimates the exposed population by over 90% accuracy. Therefore, the proposed method is recommended to be utilized as an efficient tool of community resilience assessment as it is highly accurate in downscaling the spatial distribution of population data.
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    A Gis-Based Simulation Application To Model Surface Runoff Level In Urban Blocks.
    (Faculty of Architecture University of Moratuwa, 2020-11) Wijayawardana, PNP; Abenayake, CC; ayasinghe, AB; Kalpana, LDCHN; Dias, N; Amaratunga, D; Haigh, R
    Simulation of flood inundation in urban areas longer important, given the magnitude of potential loss and disruption associated with non-river based, urban flooding. The complexity of the urban environment and lack of high-resolution topographic and hydrologic data compromise the development and implementation of models. Low impact development (LID) is technical know-how on a collection of sustainable practices that mimic natural hydrological functions including infiltration, evapotranspiration or use of surface runoff. Several studies have been carried out to discuss the impact of urbanization scenarios in reducing the urban flood risk in watershed scale in Sri Lanka. Yet, there is a gap remains in simulating the effectiveness of LID-based planning practices to reduce flood risk with the complex built form scenarios. In such a situation, this study attempts to make a significant contribution to simulate the variations of flood regulation functions under different high-intensive urban development scenarios, particularly focusing on the urban metropolitan regions. The analyses were carried out utilizing SWMM (Storm Water Management Model) which is open-source flood inundation simulation approach with the help of GIS in a more qualitative manner. The simulation results indicate that expanding built form scenarios increase the flood venerability for city functions, increasing inundation duration and LID scenarios able to reduce the surface runoff to reduce flood vulnerability at a significant level. The simulation results had been verified with the real ground situation (mean percentage change < 15.5%) which able to capture the thresholds of built form variation, as well as dynamic land uses and infrastructure supply which can be used as a tool for future planning practices and decision-making.
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    item: Conference-Full-text
    Landslides predictIon based on neural network and remote sensing data
    (2018) Kalpana, LDCHN; Subhashini, LDCS; Samarawickrama, S.
    Landslide is one of the main natural disaster that Sri Lankan intermediate zone faced. In most of the cases, property damage and vulnerability of people significantly high compared to the other natural disasters. This mainly occurs due to poor disaster forecasting methodologies, lack of early warning systems and preparedness practices. Therefore there is a vital need of implementing a model for landslides prediction. In this research, it supposed to introduce landslide forecasting model based on remote sensing methodologies and Artificial Neural Network (ANN). Landslide forecasting modelling has long history in many countries and most of the scenarios it was based on the trend line analysis, and liner and non-liner regression analytical methods and models. Remote sensing methodologies and technics are doing significant impact on landslides disaster evaluation and mitigation process in multiple sectors. Normalized Difference Vegetation Index (NDVI) has shown powerful calculation remote sensing techniques to identify vegetation, soil and build-up areas and significant variations of them through the raster calculation methods. Hence, these factors were rarely use in existing landslide forecasting models. This research is identified the significance of these calculations on landslide forecasting using ANN model with NDVI. The model was evaluated using a baseline model. The results of the model expose ability to increase the accuracy than existing landslides forecasting models.
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    item: Conference-Abstract
    Network centrality assessment (NCA): assessing the transport networks’ resilience for urban flooding
    (Sri Lanka Society of Transport and Logistics, 2020-11) Kalpana, LDCHN; Jayasinghe, AB; Abenayake, CC; Gunaruwan, TL
    Transport systems are vital to the functioning of urban areas. Minimizing disruptions to transport networks due to natural hazards is a key goal in disaster-resilient urban planning. This study presents a framework to assess transport networks’ resilience to urban flooding. The proposed framework is developed based on network centrality and graph theory. The study utilizes betweenness and closeness centrality to capture transport network resilience under two movement thresholds: pedestrian movements (r=1km) and vehicular movements (r=10km). The study utilizes Open Source GIS tools to compute centrality values. The case study is carried out in Greater Colombo, Sri Lanka and selects three significant urban flooding events, i.e. 2010 May 17, 2016 May 15 and 2017 May 25. It assesses the transportation network resilience in two respects. First, the topological impacts from each flood event to the transportation network. Second, the accessibility changes in the transportation system. The results reveal three key findings. First, compared with direct impacts on the transportation network (≤7%), the relative impact is significantly higher (>60%). This is particularly pronounced in vehicular movements relative to pedestrian movements, because pedestrian movement is hindered by floods where there is a loss of several road segments in a given neighbourhood. For vehicle movement, floods significantly impact the entire transportation system and their pass-by trips. Second, the study revealed redundant depreciation of the transportation accessibility as it shifts the accessibility from downtown (CBD) to suburban areas and creates temporary accessibility hotpots in certain local areas. Third, considering the statistical distribution of network centrality, the study identifies significant declines of transportation accessibility in each flooding event, significantly impacting trips of longer length (>10km) as the loss of shortest path roads segments significantly impact the pass-by movements of the transportation system. The proposed framework can be utilized as a planning tool to assess transport network resilience and devise precautionary measures to mitigate disaster risk.