The Development of a Novel Decision Support System for the Location of Green Infrastructure for Stormwater Management

Kazak, Jan; Chruściński, Jakub; Szewrański, Szymon

doi:10.3390/su10124388

Cited by 52 publications

(28 citation statements)

References 86 publications

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“…Progressing urbanization and climate change are interacting processes that require comprehensive actions to minimize their negative effects. Particularly significant are the challenges related to the intensity of threats of the progressive dispersion of housing construction along its communication routes (urban sprawl) and the increasing socio-environmental fragmentation of urban areas [1][2][3]. These threats often result from a lack of knowledge and in-depth analysis at the local level, starting from a single building, through housing estates, and ending in the areas of basic territorial units; i.e., municipalities.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

et al. 2020

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Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.

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Section: Introductionmentioning

confidence: 99%

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

et al. 2020

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“…These methods can also be used to determine the parameters of their own catchments (direct catchments of the pit) that are necessary for hydrological calculations. LiDAR data, digital terrain model (DTM) data, or digital elevation model (DEM) data, as well as aerial photographs (orthophotomaps), may also be used, among others, in predicting the retention capacity of reservoirs and polders [23,82], in constructing decision support systems for the purposes of stormwater management [83][84][85], or to model the risk of occurrence and range of floods [86][87][88].…”

Section: Introductionmentioning

confidence: 99%

The Use of Digital Terrain Models to Estimate the Pace of Filling the Pit of a Central European Granite Quarry with Water

Jawecki

Szewrański

Stodolak

et al. 2019

Water

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This paper presents the results of an analysis of the pace of filling one of the deepest European granite quarries with water. A DTM (digital terrain model) based on data from LiDAR ALS (light detection and ranging airborne laser scanning) was used to create a model of the pit of the Strzelin I granite quarry and to determine the reach and surface area of the direct catchment of the excavation pit. The increase in the volume of water in the excavation pit was determined. Analogue maps and DTM were used to calculate the maximum depth of the pit (113.3 m), its surface area (9.71 ha), and its capacity (5.1 million m3). The volume of water collected in the excavation pit during the years 2011–2018 was determined based on the analogue base map and the DTM. The result was 0.335 million m3. Based on the data made available by the mining company, the correlation of the DTM with the orthophotomap of the mining area and additional field measurements, the ordinates of the water level in the years 2011–2018 were determined. Initially, the water surface level in the quarry was located on the ordinate of 66.6 m a.s.l. (July 20, 2011). After the pumping of water was discontinued, the level rose to 96.1 m a.s.l. (January 28, 2018). The increase in the water volume in the quarry pit during specific periods was determined (actual retention increase). The obtained data on the volume of the retained water referred to the period during which it accumulated in the quarry. On average, the net increase in water retention in the excavation pit was 138.537 m3∙d−1, and the calculated net supply from the direct catchment (16.04 ha) was 101.758 m3∙d−1. The use of DTM and measurements of the water level in the excavation pit seem to be an efficient means of estimating the pace of spontaneous filling of the quarry with water supplied from the direct physiographic catchment.

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“…Methods for quantitatively assessing flood disaster risk are often based upon numerical simulations, such as hydrological model and hydrodynamic model (Dang and Kumar 2017;Abdulrazzak et al 2019). There are many commonly used models, such as SCS-CN (Kazak et al 2018), SWMM (Huang and Jin 2019), DRAINMOD (Youssef et al 2018), and so on. These models typically require various types of data such as rainfall data, basic geographic data and socio-economic data (Wu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing urban flood disaster risk using Bayesian network model and GIS applications

Shen

Wang

et al. 2019

Geomatics, Natural Hazards and Risk

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With global climate change, cities face the challenge of increasing flood disaster caused by heavy rainfall, and the prediction and assessment of flood disaster risk is a crucial step towards risk mitigation and adaptation planning. In this study, a method combining Bayesian network (BN) model and geographic information system (GIS), which can capture the potential relationships between factors impacting flood disaster and has capacity of quantifying uncertainty and utilizing both data and knowledgebased sources, was proposed to assess flood disaster risk. The proposed methodology was applied in a case study to assess flood disaster risk and to diagnose the reason for flood disaster in Zhengzhou City, and the results were validated by comparing with actual situation. The results show that that the relative error of very-low, low, moderate, high and very-high risk predicted by the proposed model is 12.57%, 13.21%, 2.23%, 19.63% and 21.65%, respectively, which demonstrates the discriminative power of the established model. Based on the spatial distribution of different risk levels, it can be recognized that the flood disaster risk in Zhengzhou City is decreasing from the middle to the surroundings. The results provide some basis for the field control and management of urban flood disaster.

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The Development of a Novel Decision Support System for the Location of Green Infrastructure for Stormwater Management

Cited by 52 publications

References 86 publications

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

The Use of Digital Terrain Models to Estimate the Pace of Filling the Pit of a Central European Granite Quarry with Water

Assessing urban flood disaster risk using Bayesian network model and GIS applications

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