Tool For Land Use Planning Research Articles

The 50-year spatial transition of suburban industrial parks and impacts on sustainable urbanization in the Tokyo metropolitan area

Suburban Industrial Parks (SIP) promote orderly industrial land agglomeration, contributing to sustainable land use transition. Previous studies mostly focused on socio-economic effectiveness and environmental strategies, while the multiscale spatial transition of SIPs and their roles in sustainable urbanization were not thoroughly examined. This study analyzes the temporal pattern of spatial features (TPSF) of SIPs in the Tokyo Metropolitan Area (TMA) using an original suburban industrial community (SIC) model that integrates SIP with its neighborhood. We investigated 110 SIPs established since 1959 across three investigation periods. The results reveal: 1) SIPs influence industrial land use in their neighborhood areas, mostly ranging from 500 to 2500 m. 2) SIPs’ effects on industrial land agglomerations vary over time, demonstrating three development phases: the first phase exhibits the strongest intensity of agglomeration; the second phase falls to the weak; and the third phase recovers a strong agglomeration. 3) Multiscale industrial agglomeration patterns show that 56 % of SIPs in the TMA contribute to orderly spatial transitions; 4) Main road networks facilitate industrial agglomeration. This study proposes the importance of adaptive land use management strategies for SIPs by development phases, and the SIC model could be an applicable tool for land use planning in metropolitan suburbs.

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Google map and canva for spatial understanding of grade 12 social studies students of SMAN 1 Warung Kiara, Sukabumi District

Background: Land use planning is a fundamental aspect of the Geography education for high school students, involving the organization of geographical space across economic, social, and cultural dimensions. A comprehensive grasp of this subject is imperative for students to comprehend the optimal utilization and management of geographical space. Methods: This study investigates the efficacy of Google Maps satellite mode and Canva as instructional tools for land use planning among senior high school students at SMAN 1 Warung Kiara, Sukabumi Regency. Google Maps satellite mode provides visual representations of planning areas, while Canva facilitates the creation of visually appealing and comprehensible land use planning designs. A descriptive quantitative approach was employed, involving questionnaires and interviews with a purposive sample of 32 students. Findings: The findings indicate that 75% of a students found learning with Google Maps satellite mode and Canva to be more engaging and comprehensible compared to traditional methods. Conclusion: Interviews further revealed an enhancement in students' understanding of land use planning concepts following exposure to these media. The utilization of Google Maps satellite mode and Canva emerges as an effective instructional strategy for enhancing students' comprehension of land use planning, underscoring the potential of technology and visual design in geography education.

Landslide mapping using geospatial techniques: A case study of the Bokoya Massif (Central Rif, Morocco)

The terrestrial zone of Al-Hoceima National Park is faced with a multitude of natural and man-made hazards that threaten the stability of the soil and the ecological balance: earthquakes, coastal erosion, floods, fires and landslides. The creation of landslide susceptibility maps is a crucial step in making informed decisions for preventing and mitigating landslide risks. This work focuses on the analysis of landslide potential in the Bokoya massif, located in the central Moroccan Rif, using the Analytical Hierarchical Process (AHP). Our aim is to develop a landslide probability map, which will be an important tool for land-use planning and risk management, helping to identify priority areas for further research.

From data to decisions: Empowering brownfield redevelopment with a novel decision support system

This research evaluates a novel decision support system (DSS) for planning brownfield redevelopment. The DSS is implemented within a web-based geographical information system that contains the spatial data informing three modules comprising land use suitability, economic viability, and ground risk. Using multi-criteria decision analysis, an evaluation was conducted on 31,942 ha of post-industrial land and around Liverpool, UK. The representativeness and credibility of the DSS outputs were evaluated through user trials with fifteen land-use planning and development stakeholders from the Liverpool City Region Comined Authority. The DSS was used to explore land use planning scenarios and it could be used to support decision making. Our research reveals that the DSS has the potential to positively inform the identification of brownfield redevelopment opportunities by offering a reliable, carefully curated, and user-driven digital evidence base. This expedites the traditionally manual process of conducting assessments of land suitability and viability. This research has important implications for assessing the impact of current and future planning policy and the potential for the use of digital tools for land use planning and sustainability in the UK and globally.

Insights into large landslide mechanisms in tectonically active Agadir, Morocco: The significance of lithological, geomorphological, and soil characteristics

Landslide susceptibility assessment is crucial for land use planning, infrastructure development, and hazard mitigation, particularly in tectonically active regions where lithology and soil characteristics significantly influence slope stability. This study aims to evaluate landslide susceptibility in the Agadir Ida-Ou-Tanane province, a tectonically active area, using a hierarchical multicriteria analysis approach. Eight factors influencing landslide occurrence were considered: lithology, slope, LULC, distance to main faults, hypsometry, drainage density, road density, and aspect. Pairwise comparison matrices and expert opinion were employed to determine the relative importance of these factors. The consistency ratio (CR) was calculated to ensure the reliability of the judgments. The resulting landslide susceptibility map reveals a spatial distribution ranging from very low to very high susceptibility zones. Lithology, mainly clay, and limestone formations, emerged as the most influential factor due to the active tectonic context of the study area. The model was validated using observed landslide locations and the Area Under the Curve (AUC) method, yielding a reasonable validation accuracy. The findings indicate that integrating these factors, with a particular emphasis on lithology and soil characteristics, effectively identifies areas prone to landslides, making the resulting map a valuable tool for land use planning and risk management in the Agadir Ida-Ou-Tanane region. This study contributes to understanding landslide susceptibility in tectonically active areas and provides a practical foundation for future research and decision-making. Regularly monitoring and updating the landslide susceptibility map is necessary to ensure its continued relevance and utility for stakeholders.

Comprehensive landslide susceptibility map of Central Asia

Abstract. Central Asia is an area characterized by complex tectonics and active deformation; the related seismic activity controls the earthquake hazard level that, due to the occurrence of secondary and tertiary effects, also has direct implications for the hazard related to mass movements such as landslides, which are responsible for an extensive number of casualties every year. Climatically, this region is characterized by strong rainfall gradient contrasts due to the diversity of climate and vegetation zones. The region is drained by large, partly snow- and glacier-fed rivers that cross or terminate in arid forelands; therefore, it is also affected by a significant river flood hazard, mainly in spring and summer seasons. The challenge posed by the combination of different hazards can only be tackled by considering a multi-hazard approach harmonized among the different countries, in agreement with the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. To this aim the most detailed landslide inventories, covering both national and transboundary territories, were implemented in a random forest model, together with several independent variables. The proposed approach represents an innovation in terms of resolution (from 30 to 70 m) and extension of the analyzed area with respect to previous regional landslide susceptibility and hazard zonation models applied in Central Asia. The final aim was to provide a useful tool for land use planning and risk reduction strategies for landslide scientists, practitioners, and administrators.

Index Measuring Land Use Intensity—A Gradient-Based Approach

To monitor the changes in the landscape, and to relate these to ecological processes, we need robust and reproducible methods for quantifying the changes in landscape patterns. The main aim of this study is to present, exemplify and discuss a gradient-based index of land use intensity. This index can easily be calculated from spatial data that are available for most areas and may therefore have a wide applicability. Further, the index is adapted for use based on official data sets and can thus be used directly in decision-making at different levels. The index in its basic form consists of two parts where the first is based on the data of buildings and roads and the second of infrastructure land cover. We compared the index with two frequently used ‘wilderness indices’ in Norway called INON and the Human Footprint Index. Our index captures important elements of infrastructure in more detailed scales than the other indices. A particularly attractive feature of the index is that it is based on map databases that are updated regularly. The index has the potential to serve as an important tool in land use planning as well as a basis for monitoring, the assessment of ecological state and ecological integrity and for ecological accounting as well as strategic environmental assessments.

Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam

Landslide is a severe geohazard in many mountainous areas of Vietnam during the rainy season. They directly threaten human lives and properties every year. Landslide susceptibility maps are useful tools for risk mitigation, land-use planning, and early warning systems for local areas. It is necessary to update these maps continuously because of the complexity of landslide events. This fact requires further extending the approach techniques with practical implications. Therefore, this study aimed to develop landslide susceptibility prediction maps based on advanced machine learning (ML) techniques. Five state-of-the-art hybrid ML models were developed: bagging MLP, dagging MLP, decorate MLP, rotation forest MLP, and random subspace MLP with multilayer perceptron (MLP) as a base classifier. Sixteen causative factors were collected to build landslide susceptibility maps based on the relationship between historical landslide locations and specific local geo-environmental conditions. The model performance was verified using various statistical indexes. Based on the area under ROC curve (AUC) analysis results of the testing dataset, the rotation forest MLP model has the greatest predictive accuracy of AUC = 0.818. It is followed by the decorate MLP and bagging MLP (AUC = 0.804), the random subspace MLP model (AUC = 0.796), the dagging MLP (AUC = 0.789), and the single MLP (AUC = 0.698). The results of this study can be applied effectively to other mountainous regions to mitigate the risk of landslides.

Mapping land suitability for maize (Zea mays L.) production using GIS and AHP technique in Zimbabwe

The study integrates geographic information system (GIS) and analytic hierarchy process (AHP) to evaluate land suitability for maize production in Zimbabwe using multi-criteria evaluation (MCE) process. Four thematic maps based on rainfall, temperate, soil type and slope were integrated through overlay technique in a GIS environment to produce maize production suitability map. The resultant maize suitability map was overlaid with constraints map to ‘mask out’ all non-agricultural land. The final maize suitability map shows that 3.20% of the total land is highly suitable, 16.56% is suitable, 25.34% is moderately suitable, 32.33% is marginally suitable and 9.57% is not suitable for maize production in its current form. The maize suitability classification was validated by regression analyses using measured maize grain yield of 5 key maize varieties representing 5 different maturity groups. Grain yield was regressed against suitability index (SI) of each land class. There were significant positive correlations between maize grain yield and land suitability classes (R2 = 0.63 - 0.85). Integrating GIS and AHP with MCE is effective in assessing land suitability for targeting location specific interventions for maize production and the result is a comprehensive suitability map for Zimbabwe, incorporating several critical environmental factors affecting maize adaptation. We recommend the use of this suitability map as a decision support tool in land use planning and policy making.

Participatory land resources planning to promote sustainable landscape management in rainfed areas-Morocco

Land degradation and desertification affect around one-third of the land used for agriculture, undermining productivity, and farmers' livelihoods, and resilience. People in rainfed and dryland areas are the most vulnerable to the environmental and social impacts of degradation, yet opportunities to expand agricultural areas in order to meet the needs of an ever-growing population are limited and/or not feasible. On the bright side, there are opportunities to avoid, reduce and reverse land degradation (LD), however, it will require identifying areas that are suited to implement sustainable land management (SLM) practices and create an enabling environment that encourages such initiatives. In Morocco, latest available data from 2015 showed that 19 percent of its land over the total land area is degraded. In order to assess the level of degradation in rainfed landscapes of Morocco and identify the appropriate technological solutions in response to the driving forces and pressures, and to come up with a territorial planning strategy, analyses were carried out at different levels to identify the land degradation “hot spots” as well as the SLM “bright spots.” This paper sheds light on the decision support framework, co-developed with partners and applied in Morocco, the findings from national and subnational assessments of LD, as well as the process of participatory planning in selected communities of the Souss-Massa region for evaluation and scaling up of SLM good practices. The process led to the development of a “Territorial Planning Pact” as a tool for land use planning and a 3-year action plan to help decision-makers implement and mainstream SLM in the selected communities and guide similar processes throughout the region. The “Territorial Planning Pact” forms a base for planning and implementing the development program in the region led by key stakeholders. The paper presents the actions taken and concludes by highlighting the importance of integrating both biophysical and socio-economic information in a multi-level participatory process to identify the actions and responsibilities of various stakeholders and ensure sustainable management of limited natural resources and improved livelihoods for all.

European blue and green infrastructure network strategy vs. the common agricultural policy. Insights from an integrated case study (Couesnon, Brittany)

Urbanization and agricultural intensification are the main drivers of biodiversity losses through multiple stressors, especially habitat fragmentation, isolation and loss. Designing Blue and Green Infrastructure Networks (BGIN) has been recommended as a potential tool for land-use planning to increase ecosystem services while preserving biodiversity. All municipalities in France are required to perform BGIN planning. This article focuses on the Couesnon watershed (Brittany, France) and the participatory process used to define and analyze five possible pathways of future land-use and land-cover changes that included implementation of BGINs. Impacts on biodiversity were estimated by quantifying the change in landscape connectivity of woodlands, grasslands and wetlands. The effectiveness of BGIN policies was assessed by comparing current landscape connectivity (2018) to those in possible futures. Landscape connectivity referred to functional connectivity for three indicator species (Abax parallelepipedus, Maniola jurtina and Arvicola sapidus) across three landscape features: woodlands, grasslands and wetlands, respectively. Results allowed impacts of urban and agricultural land-use changes to be identified in terms of extent and quality. If BGIN policies were applied effectively to control the expansion of gray infrastructure, they would help increase the area and the quality of grassland and woodland connectivity by no more than 2%. Agricultural land-use and land-cover changes could have more impact on the extent of grassland (−82% to +38%) and wetland (−49% to +47%) connectivity. Current and future trends for hedgerows implied a decrease in woodland connectivity of 9.8–33.8%. Impacts on the quality of landscape connectivity is not proportional with the extent, as a decrease of the latter can have relatively more negative impacts on the former, and inversely. The study highlights that the BGIN strategy can preserve landscape connectivity effectively in urban ecosystems, where human density is higher, but can be threatened by agricultural intensification.

Simulating sediment discharge at water treatment plants under different land use scenarios using cascade modelling with an expert-based erosion-runoff model and a deep neural network

Abstract. Excessive sediment discharge in karstic regions can be highly disruptive to water treatment plants. It is essential for catchment stakeholders and drinking water suppliers to limit the impact of high sediment loads on potable water supply, but their strategic choices must be based on simulations integrating surface and groundwater transfers and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical descriptions for the modelling process, and they can be particularly complex to predict due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new cascade modelling approach with an erosion-runoff geographic information system (GIS) model (WaterSed) and a deep neural network. The model was used in the Radicatel hydrogeological catchment (106 km2 in Normandy, France), where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five design storms under current land use and compared to four land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices, and coupling of eco-engineering/best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes (positive dye tracing) were used as input data for the deep neural network model. The model structure was found by a classical trial-and-error procedure, and the model was trained on 2 significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly backward-chaining nested cross-validation revealed that the model is able to generalize on new datasets. Simulations made for the four land use scenarios suggested that ploughing up 33 % of grasslands would increase sediment discharge at the water treatment plant by 5 % on average. By contrast, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the ranges of 10 %–44 % and 24 %–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 % to 80 %). The cascade modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plants under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.

Evaluation Model for Sustainable Development of Settlement System

Sustainability of settlement systems is of greatest relevance in political and socio-economic stability all over the world. The development effectiveness of a rural settlement system involves the solution of a number of matters in sustainable development, namely social welfare and environmental balance, economy and industry development, improving the pipeline and utility infrastructure, and improving the efficiency of the decision-making process. Currently, the sustainability of a rural settlement system is one of the key objectives in regional planning in post-Soviet countries. The introduction of new tools for assessing and managing the settlement system development is particularly true for Belarus, as a country with a strong focus on agricultural industry. The research aim was to develop and approve a model for evaluating the settlement system development. The research methods were based on the complex and interdisciplinary approaches, namely the system-element approach, the comparative analysis, spatial and mathematical modelling, factor analysis, and the cartographic analysis. The model was approved by practical consideration for evaluating the development of the analogue object at the local planning level. The practical relevance of the research is associated with the potential for using the model as a significant tool in land use planning. The model employs both quantitative and qualitative evaluation to obtain alternative solutions towards sustainable development of rural areas. Another advantage of the model is its multifunctionality, which enables: (1) sustainability evaluation of a settlement system, (2) establishment of regional planning priority areas, and (3) development of specific measures for ensuring the sustainability of a regional settlement system and its elements.

Characterizing spatial and temporal deforestation and its effects on surface urban heat islands in a tropical city using Landsat time series

With 1.76 million inhabitants, Mérida city has recently become the most populated city in southeast Mexico, being one of the most attractive cities for investment in real-estate projects. This has increased deforestation to the periphery, resulting in heat islands. We used time series of Landsat images and the BFAST algorithm to analyze annual deforestation around Mérida city over the 2000–2018 period. Land surface temperature was also estimated using Landsat images to compare temperatures before and after deforestation and examine heat islands in the city. The deforestation maps had a 96.82% overall accuracy on average, user’s and producer’s accuracy values of 91.62% and 95.46%, respectively, and an estimated total deforested area of 5413 ha over the study period. Land surface temperature increased in 2.36–3.94 °C after deforestation, and heat islands of varying intensity were detected in 80% of the urban territory, mainly where deforestation occurred. Our results demonstrate the effectiveness of Landsat images and the BFAST algorithm for detecting deforestation in peri-urban spaces, as well as the usability of Landsat images for estimating land surface temperature. These images are effective tools for urban land-use planning and for examining the formation of heat islands in tropical cities.

Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

Consequences of underexplored variation in biodiversity indices used for land-use prioritization.

For biodiversity protection to play a persuasive role in land-use planning, conservationists must be able to offer objective systems for ranking which natural areas to protect or convert. Representing biodiversity in spatially explicit indices is challenging because it entails numerous judgments regarding what variables to measure, how to measure them, and how to combine them. Surprisingly few studies have explored this variation. Here, we explore how this variation affects which areas are selected for agricultural conversion by a land-use prioritization model designed to reduce the biodiversity losses associated with agricultural expansion in Zambia. We first explore the similarity between model recommendations generated by three recently published composite indices and a commonly used rarity-weighted species richness metric. We then explore four underlying sources of ecological and methodological variation within these and other approaches, including different terrestrial vertebrate taxonomic groups, different species-richness metrics, different mathematical methods for combining layers, and different spatial resolutions of inputs. The results generated using different biodiversity approaches show very low spatial agreement regarding which areas to convert to agriculture. There is little overlap in areas identified for conversion using previously published indices (mean Jaccard similarity, Jw , between 0.3 and 3.7%), different taxonomic groups (5.0%<mean Jw <13.5%), or different measures of species richness (15.6%<mean Jw <33.7%). Even with shared conservation goals, different methods for combining layers and different input spatial resolutions still produce meaningful, though smaller, differences among areas selected for conversion (40.9%<mean Jw <67.5%). The choice of taxonomic group had the largest effect on conservation priorities, followed by the choice of species richness metric, the choice of combination method, and finally the choice of spatial resolution. These disagreements highlight the challenge of objectively representing biodiversity in land-use planning tools, and present a credibility challenge for conservation scientists seeking to inform policy making. Our results suggest an urgent need for a more consistent and transparent framework for designing the biodiversity indices used in land-use planning, which we propose here.

Calculations on Ecological Footprint as a tool for land use planning and development on V4 countries

International online peer-reviewed open-access journal offers a possibility for the international community of professionals working in the fields of regional and rural development or tourism to exchange their ideas and research results or practical achievements as it publishes results of both theoretical and applied research in these fields.

Nature-Based Solutions for Storm Water Management—Creation of a Green Infrastructure Suitability Map as a Tool for Land-Use Planning at the Municipal Level in the Province of Monza-Brianza (Italy)

Growing and uncontrolled urbanization and climate change (with an associated increase in the frequency of intense meteoric events) have led to a rising number of flooding events in urban areas due to the insufficient capacity of conventional drainage systems. Nature-Based Solutions represent a contribution to addressing these problems through the creation of a multifunctional green infrastructure, both in urban areas and in the countryside. The aim of this work was to develop a methodology to define Green Infrastructure for stormwater management at the municipal level. The methodology is defined on the basis of three phases: the definition of the territorial information needed, the production of base maps, and the production of a Suitability Map. In the first phase, we define the information needed for the identification of non-urbanized areas where rainwater can potentially infiltrate, as well as areas with soil characteristics that can exclude or limit rainwater infiltration. In the second phase, we constructed the following base maps: a “map of green areas”, a “map of natural surface infiltration potential” and a “map of exclusion areas”. In phase 3, starting from the base maps created in phase 2 and using Geographical Information Systems’ (GIS) geoprocessing procedures, the “Green area compatibility map to realize Green Infrastructure”, the “map of areas not suitable for infiltration” and the final “Green Infrastructure Suitability Map” are created. This methodology should help municipal authorities to set up Green Infrastructure Suitability Maps as a tool for land-use planning.

An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels

With the rapid urbanization, urban underground utility tunnels have seen fast growth in China in the past few years. Urban utility tunnels can house various kinds of city ‘lifelines’ such as natural gas pipeline, heat pipeline, water supply system, sewer pipeline, electricity and telecommunication cables, which are of great significance to guarantee essential flows of energy, information and logistics for urban life. If a utility tunnel accident occurs, the consequences could be catastrophic. Risk assessment has been an important tool to examine the safety performance of industrial facilities and the effectiveness of safety measures. In this study, an integrated model based on dynamic hazard scenario identification (DHSI), Bayesian network (BN) modeling and risk analysis is proposed for risk assessment of urban utility tunnels. The worst-case scenario of urban utility tunnel accidents is identified by DHSI and modelled by BN. Meanwhile, risk analysis is conducted based on the results of BN considering casualties and economic losses. Finally, the integrated method is applied to evaluate the risk level of a real-world utility tunnel. The results indicate that the integrated quantitative risk assessment framework is an alternative and effective tool for safety assessment and land-use planning of urban utility tunnels.