Deliverable 6.7 describing the GeoViz application for GeoData visualisation, developed for the EU-funded project ILIAD.
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 147-154, 2014www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/147/2014/doi:10.5194/isprsarchives-XL-8-147-2014Integrated flood disaster management and spatial information: Case studies ofNetherlands and IndiaS. Zlatanova1, T. Ghawana2, A. Kaur2, and J. M. M. Neuvel31Faculty of Architecture, Jullianalaan, TU Delft, 134, 2628BL Delft, the Netherlands2Centre for Disaster Management Studies, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, P.O. Box-110078, India3Saxion University of Applied Sciences, Risk management, Handelskade 75, 7417 DH Deventer, the NetherlandsKeywords: Floods, Spatial Information Infrastructure, GIS, Risk Management, Emergency Management Abstract. Spatial Information is an integral part of flood management practices which include risk management &emergency response processes. Although risk & emergency management activities have their own characteristics, forexample, related to the time scales, time pressure, activities & actors involved, it is still possible to identify at least onecommon challenge that constrains the ability of risk & emergency management to plan for & manage emergencieseffectively and efficiently i.e. the need for better information. Considering this aspect, this paper explores flood managementin Netherlands& India with an emphasis on spatial information requirements of each system. The paper examines theactivities, actors & information needs related to flood management. Changing perspectives on flood management inNetherlands are studied where additional attention is being paid to the organization and preparation of flood emergencymanagement. Role of different key actors involved in risk management is explored. Indian Flood management guidelines, byNational Disaster Management Authority, are analyzed in context of their history, institutional framework, achievements andgaps. Flood Forecasting System of Central Water Commission of India is also analyzed in context of spatial dimensions.Further, information overlap between risk & emergency management from the perspectives of spatial planners & emergencyresponders and role of GIS based modelling / simulation is analyzed. Finally, the need for an integrated spatial informationstructure is explained & discussed in detail. This examination of flood management practices in the Netherlands and Indiawith an emphasis on the required spatial information in these practices has revealed an increased recognition of the stronginterdependence between risk management and emergency response processes. Consequently, the importance of anintegrated spatial information infrastructure that facilitates the process of both risk and emergency management isaddressed.Conference Paper (PDF, 1063 KB) Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 147-154, 2014www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/147/2014/doi:10.5194/isprsarchives-XL-8-147-2014Integrated flood disaster management and spatial information: Case studies ofNetherlands and IndiaS. Zlatanova1, T. Ghawana2, A. Kaur2, and J. M. M. Neuvel31Faculty of Architecture, Jullianalaan, TU Delft, 134, 2628BL Delft, the Netherlands2Centre for Disaster Management Studies, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, P.O. Box-110078, India3Saxion University of Applied Sciences, Risk management, Handelskade 75, 7417 DH Deventer, the NetherlandsKeywords: Floods, Spatial Information Infrastructure, GIS, Risk Management, Emergency ManagementAbstract. Spatial Information is an integral part of flood management practices which include risk management &emergency response processes. Although risk & emergency management activities have their own characteristics, forexample, related to the time scales, time pressure, activities & actors involved, it is still possible to identify at least onecommon&
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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National forestry Commission (SBB) and National Park De Biesbosch. Subcontractor through NRITNational parks with large flows of visitors have to manage these flows carefully. Methods of data collection and analysis can be of help to support decision making. The case of the Biesbosch National Park is used to find innovative ways to figure flows of yachts, being the most important component of water traffic, and to create a model that allows the estimation of changes in yachting patterns resulting from policy measures. Recent policies oriented at building additional waterways, nature development areas and recreational concentrations in the park to manage the demands of recreation and nature conservation offer a good opportunity to apply this model. With a geographical information system (GIS), data obtained from aerial photographs and satellite images can be analyzed. The method of space syntax is used to determine and visualize characteristics of the network of leisure routes in the park and to evaluate impacts resulting from expected changes in the network that accompany the restructuring of waterways.
The Dutch main water systems face pressing environmental, economic and societal challenges due to climatic changes and increased human pressure. There is a growing awareness that nature-based solutions (NBS) provide cost-effective solutions that simultaneously provide environmental, social and economic benefits and help building resilience. In spite of being carefully designed and tested, many projects tend to fail along the way or never get implemented in the first place, wasting resources and undermining trust and confidence of practitioners in NBS. Why do so many projects lose momentum even after a proof of concept is delivered? Usually, failure can be attributed to a combination of eroding political will, societal opposition and economic uncertainties. While ecological and geological processes are often well understood, there is almost no understanding around societal and economic processes related to NBS. Therefore, there is an urgent need to carefully evaluate the societal, economic, and ecological impacts and to identify design principles fostering societal support and economic viability of NBS. We address these critical knowledge gaps in this research proposal, using the largest river restoration project of the Netherlands, the Border Meuse (Grensmaas), as a Living Lab. With a transdisciplinary consortium, stakeholders have a key role a recipient and provider of information, where the broader public is involved through citizen science. Our research is scientifically innovative by using mixed methods, combining novel qualitative methods (e.g. continuous participatory narrative inquiry) and quantitative methods (e.g. economic choice experiments to elicit tradeoffs and risk preferences, agent-based modeling). The ultimate aim is to create an integral learning environment (workbench) as a decision support tool for NBS. The workbench gathers data, prepares and verifies data sets, to help stakeholders (companies, government agencies, NGOs) to quantify impacts and visualize tradeoffs of decisions regarding NBS.
