A spatial decision framework for sustainable desalination: Mapping optimal sites for zero liquid discharge in Europe

Successful scaling of agricultural technology requires a spatial explicit framework for targeting the right variety at the right place. This entails a multi-criteria evaluation (MCE) approach, using a set of determining factors to delineate the scaling domains for faba bean (Vicia faba L.) varieties in and identify potentially suitable land area in a specific region, and zone in Ethiopia. Meeting this challenge will require a solid spatial framework. Land suitability analysis is an evaluation and spatial decision making, involving several determining factors. The factors considered in this analysis include key biophysical parameters such as climate, topography, soil types and properties. The analysis was also focused on improved faba bean varieties viz., Dagm, Dosha, Gabelcho, Gora, Hachalu, Moti and Walki. The environmental factors’ layers of a specific crop pixel values were classified and given a weight, and then compared among themselves for further ranking to account for their relative importance to delineate variety specific extrapolation domains. The geo-statistical analysis was carried out to estimate the extent of the scalable areas. The classification showed that, it was highly suitable for varieties 0.02 million hectares for Dosha; 0.19 for Gabelcho; 0.11 for Gora; 0.33 for Moti; 0.05 for Dagm; 0.14 for Hachalu; and 0.26 million hectares for Walki. Moderately suitable areas for these varieties covered 5.0, 9.4, 7.2, 15.3, 4.6, 8.8, and 7.5 million hectares, respectively across the country. The largest proportion for all varieties was moderately suitable; while the share of slightly suitable was very low, although there was quite variability within each of the faba bean variety in terms of its agro-ecology adaptation to the target environments. Such biophysical spatial frameworks become essential entry points for introducing variety specific product profiles and this can be further enhanced by incorporating socio-economic attributes accounting for return of the investment in targeting the technology.Key words: Environmental factors, spatial decision

In the last few decades, there has been a growing interest in effectively incorporating the analytic modeling capabilities of decision support systems and the spatial modeling capabilities of geospatial information systems to solve complex spatial decision-making problems in various fields. Spatial decision support systems assist decision makers in exploring, structuring, and generating solutions for complicated spatial decision problems such as apartment selection. The selection of an apartment is a decision which plays an important role in human life. The good location is the critical factor that affects the value and potential of a real estate. This emphasizes the significance of spatial factors in decision making in real estate business. The spatial accessibility value of each apartment to different service categories can be used while choosing the most suitable apartment. Hence, the study covers not only non-spatial aspects, for example, unit price, house size, and number of rooms, but also spatial aspects, such as spatial accessibility, of the apartment selection. To sum up, this study proposes a spatial decision framework, called EMEKLI, to facilitate the decision-making process for the selection of an apartment in the presence of different priorities and uncertainties among the decision criteria. Furthermore, the recommendations obtained from the decision-making process are shared with the decision makers in the 3D environment through a virtual globe.

A spatial temporal decision framework for adaptation to sea level rise

A multi-criteria spatial evaluation framework to optimise the siting of freight consolidation facilities in inner-city areas

Evaluation of potential irrigation expansion using a spatial fuzzy multi-criteria decision framework

A spatial assessment framework for evaluating flood risk under extreme climates

The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues. This paper presents a conceptual framework suitable for spatial planning in human dominated environments supported by landscape ecological thinking. It seeks to facilitate the integration of ecosystem services into current practice, including landscape metrics as suitable indicators. A literature review supported the revision of existing open questions pertaining to ecosystem services as well as their integration into landscape ecology and spatial planning. A posterior reflection of the current state-of-the-art was then used as a basis for developing the spatial planning conceptual framework. The framework is articulated around four phases (characterisation, assessment, design, and monitoring) and three concepts (character, service, and value). It advocates integration of public participation, consideration of “landscape services”, the inclusion of ecosystem disservices, and the use of landscape metrics for qualitative assessment of services. As a result, the framework looks to enhance spatial planning practice by providing: (i) a better consideration of landscape configuration in the supply of services (ii) the integration of anthropogenic services with ecosystem services; (iii) the consideration of costs derived from ecosystems (e.g. disservices); and (iv) an aid to the understanding of ecosystem services terminology for spatial planning professionals and decision makers.

Decision makers perceive the decision-making processes for solving complex spatial problems as unsatisfactory and lacking in generality. Current spatial decision support systems (SDSS) fulfill their specific objectives, but fail to address many of the requirements for effective spatial problem solving, as they are inflexible, complex to use and often domain-specific. As technology progresses, there is an increasing opportunity for the use of SDSS in a number of domains. Flexible support for spatial decision-making to solve complex, semi-structured or unstructured spatial problems can offer advantages to individuals and organisations. This research attempts to overcome problems identified in the fields of spatial decision-making and SDSS. It synthesises ideas, frameworks and architectures from geographic information systems (GIS), decision support systems (DSS) and SDSS. Concepts from spatial modelling, model and scenario life cycle management, knowledge management and multi-criteria decision-making (MCDM) methodology are explored and leveraged in the implementation of a flexible spatial decision support system (FSDSS) using object-oriented concepts and technologies. As part of the research, we proposed a generic spatial decision-making process, developed a domain-independent FSDSS framework and architecture to support this process. We also implemented a prototypical FSDSS that acts as a proof of concept for the spatial decision-making process, FSDSS framework and architecture. The proposed spatial decision-making process and the implemented FSDSS were successfully evaluated through five scenarios across spatial decision problem domains including location, allocation, routing, layout, and spatio-temporal.

Modelling infectious diseases is a complex and multi‐disciplinary problem that necessitates the combined use of multicriteria decision analysis (MCDA) and machine learning (ML) in a spatial framework. This research attempts to demonstrate the extensive applications of MCDA in the field of public health and to illustrate its utility with the combined use of spatial models and machine learning. The study investigates the risk factors for communicable diseases with a focus on vector‐borne infectious diseases, such as West Nile Virus (WNV), malaria, dengue, etc. It aims to quantify vector‐borne disease risk by examining the geographic contextual effects of socio‐economic, climatic, and environmental factors using the objective‐weighting technique adopted from MCDA and machine learning in a geographic information systems (GIS) framework. The authors attempted to minimize subjective bias from the decision space by utilizing an objective‐weighted technique to quantify the risk. The study adopted Shannon's entropy to derive weights for each factor and its classes. The derived weighted layers are fed to an artificial neural network to obtain a final map of risk susceptibility. This final risk map allows policymakers to examine vulnerable areas and identify the factors pivotal to the contribution of risk. Findings show the traffic volume as the most influential variable, and terrain slope as the least one in the disease spread for the study area. The risk appears to be concentrated and distributed along vegetation, wetlands, and around water bodies. The results produced by ensemble learning show great promise with more than 94% accuracy. The accuracy of the results was determined by the confusion matrix and the kappa index of agreement (KIA). The vector control programmes need to adapt to better manage the dynamic changes in patterns involving vector‐borne infectious diseases.

<p>Flooding is the number one natural disaster in terms of insured and uninsured losses annually. The development of reliable methods for flood simulation have greatly improved our ability to predict floods thereby reducing damages and loss of life in flood-prone regions. However, there is still a lot of room for improvement and innovation to provide better predictions, especially for flash floods, particularly in urban areas  This is addressed in the present study, the goal of which it is to improve simulation and prediction of flash floods and to develop a spatial decision-making model for implementing flood protection measures. In this regard, different approaches for flood simulation and flood protection should be applied. The proposed methodology links flood hazard modeling, remote sensing and machine learning methods. Combining these physical models and data driven methods will result in a more reliable hybrid model that can be employed for prediction of (flash) floods and event analysis. In order to achieve the research goal of present study we: i) add more functionality to a hydrodynamic model code; ii) complement the latter with data driven methods ;iii) develop a spatial decision-making model framework for defining flood protection measures, iv) validate process-based and data driven methods, and finally v) cross-evaluate Light Detection And Radar (LiDAR) topography with available local super-resolution drone data to assess the ability to incorporate local flood defenses into the models. The most important outcome is the creation of valuable flood maps in areas where it matters - while accounting for effects of land use and climate change. This will serve scientists as well as land and risk management authorities with better actionable flood risk information in locations where people and assets are located and in danger. It also develops innovative methodologies for estimating the changing risk from flash floods based on land use scenarios and climate change projections. Moreover, developing spatial multi-criteria decision making (SMCDM) can help decision makers to determine suitable locations and methods for flood protection measures. These methods will be particularly valuable in the context of solving current challenges of accounting for and mitigating flash floods and the effects of climate change.</p>

Railway networks are highly susceptible to land subsidence, which can undermine their functional stability and safety, resulting in recurring failures and vulnerabilities. This paper aims to evaluate the susceptibility of the railway network due to Qanat underground channels in the city of Bafq, Iran. The criteria considered for assessing the susceptibility of Qanats subsidence on the railway network in this study are Qanat channel density, Qanat well density, discharge rate of the Qanat, depth of the Qanat, railway traffic, and the railway passing load. The subjective determination of criteria weights in Multi-Criteria Decision-Making (MCDM) for susceptibility analysis is typically a complex, time-consuming, and biased task. Furthermore, there is no comprehensive study on the impact and relative significance of Qanat-related factors on railway subsidence in Iran. To address this gap, this study developed a novel spatial objective weighting approach based on Principal Component Analysis (PCA)—as an unsupervised Machine Learning (ML) technique—within a spatial decision-making framework specifically designed for railway susceptibility assessment. In the proposed framework, the final Qanat-induced subsidence susceptibility zoning was conducted using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This study identified 7.7 km2 of the total area as a high-susceptibility zone, which encompasses 15 km of railway network requiring urgent attention. The developed framework demonstrated promising performance without deploying subjective information, providing a robust data-driven approach for susceptibility assessment in the study area.

In response to shoreline erosion and potentially more severe storm damage due to climate change and sea level rise, armouring of shorelines using traditional hard structures is likely to increase. An emerging alternative to seawalls and other hard structures is to create ‘living shorelines’ where natural habitats are incorporated into a resilient shoreline stabilization design. Research has shown that functional, multiuse living shorelines provide options for reducing erosion rates and sustaining shoreline stability while supporting intertidal and nearshore habitat. Drawing upon the scientific literature, shoreline management best practices, and the results from an expert opinion survey, we propose a spatial decision framework for multiclass suitability analysis of generic shoreline stabilization options with a focus on the unique challenges and opportunities of South Florida. The results have been incorporated into a web application that can facilitate decision-making in support of nature-based stabilization infrastructure.

In arid and semiarid areas in the world, including the Mediterranean region, groundwater has been widely and intensively used for irrigation over the last few decades. Practical as well as economic reasons make its use much more preferable, as compared to surface water, especially to individual farmers. Yet, this rapid and largely uncontrolled expansion in groundwater exploitation, which stimulated the socioeconomic development of numerous rural communities, has produced many negative impacts on aquifer degradation and environmental deterioration. The most common remedy to such problems is the application of specific groundwater management policies that can simultaneously meet socioeconomic and environmental protection goals. In this sense, the paper introduces a methodology for an optimal management of irrigation water, by specifically exploring the socioeconomic and environmental impacts of spatially allocated water conservation measures at the watershed level. The analysis is conducted by developing a multi-criteria decision-making framework, consisting of three distinct models: a hydrogeological, an optimization, and a multi-criteria one, which appraises the results of the other two. The proposed methodology is presented through a case study at a rural Greek watershed, in which groundwater is the sole water source for an intensively practiced agriculture. A system of water use quotas is the resource conservation policy instrument that is examined under a decision-making approach. Results show that some specifically designed and spatially non-uniform quota allocation schemes can meet in an optimum way the relevant criteria.

In practice, most planners do not make significant use of planning support systems. Although significant research has been conducted, the focus tends to be on supporting individual tasks, and the outcomes are often the development of new stand-alone tools that are difficult to integrate into existing workflows. The knowledge contribution in this paper focuses on developing a novel spatial decision support framework focusing on the workflows and tool-chains that span across different teams within an organisation, with varying skill sets and objectives. In the proposed framework, the core decision-making process uses set decision parameters that are combined using a weighted decision tree. The framework is evaluated by developing and testing tool-chains for a real-world land suitability case study. The tool-chain was implemented on top of a GIS platform.

Climate change risk reduction requires cities to undertake urgent decisions. One of the principal obstacles that hinders effective decision making is insufficient spatial knowledge frameworks. Cities climate adaptation planning must become strategic to rethink and transform urban fabrics holistically. Contemporary urban planning should merge future threats with older and unsolved criticalities, like social inequities, urban conflicts and “drosscapes”. Retrofitting planning processes and redefining urban objectives requires the development of innovative spatial information frameworks. This paper proposes a combination of approaches to overcome knowledge production limits and to support climate adaptation planning. The research was undertaken in collaboration with the Metropolitan City of Venice and the Municipality of Venice, and required the production of a multi-risk climate atlas to support their future spatial planning efforts. The developed tool is a Spatial Decision Support System (SDSS), which aids adaptation actions and the coordination of strategies. The model recognises and assesses two climate impacts: Urban Heat Island and Flooding, representing the Metropolitan City of Venice (CMVE) as a case study in complexity. The model is composed from multiple assessment methodologies and maps both vulnerability and risk. The atlas links the morphological and functional conditions of urban fabrics and land use that triggers climate impacts. The atlas takes the exposure assessment of urban assets into account, using this parameter to describe local economies and social services, and map the uneven distribution of impacts. The resulting tool is therefore a replicable and scalable mapping assessment able to mediate between metropolitan and local level planning systems.