Indicator-based Model Research Articles

Assessing the Land Reclamation Suitability of Beam Fabrication and Storage Yard in Railway Construction: An AHP-MEA Method.

Railway construction contributes to socio-economic development but causes the occupation and destruction of land resources. How to effectively restore the temporary land and achieve efficient and rational reuse therefore becomes particularly important. The beam fabrication and storage yard (BFSY), as a large temporary facility during railway construction, occupies a large area of land. However, BFSYs damage the land in the way of pressing and may harden the ground to a high degree due to the use of high-density pile foundations, adversely affecting the soil properties. Therefore, this research aims to develop a model for evaluating the land reclamation suitability (LRS) of BFSY. The LRS evaluation indicator system of BFSY was firstly constructed based on the literature review and expert interviews. Then, an indicator-based model for assessing the LRS of BFSY was developed by integrating the analytic hierarchy process (AHP) model and the matter-element analysis (MEA) model. A case project in China was chosen to demonstrate and validate the developed model, and results show that the proposed model can rationally evaluate the LRS of BFSY in railway construction. The findings of this research enrich the knowledge system of sustainable railway construction and guide construction managers to conduct practical suitability assessments of land reclamation.

A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study

ObjectivesIdentifying prognostic factors helps optimize the treatment regimen and promote favorable outcomes. We conducted a prospective cohort study on patients with pulmonary tuberculosis to construct a clinical indicator-based model and estimate its performance.MethodsWe performed a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed between 2018 and 2019 in Nanjing city as the external validation population. We generated a risk score based on blood and biochemistry examination indicators by the least absolute shrinkage and selection operator (LASSO) Cox regression. Univariate and multivariate Cox regression models were used to assess the risk score, and the strength of association was expressed as the hazard ratio (HR) and 95% confidence interval (CI). We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Internal validation was conducted by 10-fold cross-validation.ResultsTen significant indicators (PLT, PCV, LYMPH, MONO%, NEUT, NEUT%, TBTL, ALT, UA, and Cys-C) were selected to generate the risk score. Clinical indicator-based score (HR: 10.018, 95% CI: 4.904–20.468, P < 0.001), symptom-based score (HR: 1.356, 95% CI: 1.079–1.704, P = 0.009), pulmonary cavity (HR: 0.242, 95% CI: 0.087–0.674, P = 0.007), treatment history (HR: 2.810, 95% CI: 1.137–6.948, P = 0.025), and tobacco smoking (HR: 2.499, 95% CI: 1.097–5.691, P = 0.029) were significantly related to the treatment outcomes. The AUC was 0.766 (95% CI: 0.649–0.863) in the training cohort and 0.796 (95% CI: 0.630–0.928) in the validation dataset.ConclusionIn addition to the traditional predictive factors, the clinical indicator-based risk score determined in this study has a good prediction effect on the prognosis of tuberculosis.

Image- and health indicator-based transfer learning hybridization for battery RUL prediction

With the recent influx of electric vehicles and other electrical consumer products, the market has created a large demand for lithium-ion batteries, however, the non-linearity of their internal aging mechanisms still makes them difficult to predict. This study proposes a novel hybridized machine learning model that aims to be accessible to all researchers working in the field of battery prognostics prediction. To make it accessible to all, this study prides itself on using pre-trained and publicly available neural networks. To make full use of these available transferable networks, the traditional battery prognostics prediction methodology of using health indicators is hybridized with the use of images of the raw data curves. Also, this study tests the accuracy performance of the models by using images by themselves without the traditional health indicators to provide a proof of concept that these can be used independently as well An improvement of 6.72% in the remaining-useful-life (RUL) prediction accuracy was observed by simply adding image-based inputs, without the need for additional extensive pre-processing to an already existing health indicator-based model for the prediction of batteries’ RUL, highlighting the benefits of image-based inputs for use in regression tasks.

Implementing the livelihood resilience framework: An indicator-based model for assessing mountain pastoral farming systems

CONTEXTOngoing decreases in family farms and livestock numbers in European mountain areas are linked to multiple interconnected challenges. The continuity of such farms concerns society at large since they also act as landscape stewards, and their management influences the provision of ecosystem services.The livelihood resilience lens provides a means of examining how farm households respond and build their capacity to persist, to adapt to changes and shocks, and eventually transform what is understood as farming. While an increasing number of studies address livelihood resilience in different parts of the world, its link with livelihood strategies and how these enhance or erode livelihood resilience dimensions is still missing. OBJECTIVEWe built and applied an indicator-based framework to characterize the livelihood strategies of mountain livestock farming households in the Catalan Pyrenees (Spain) considering local historical trends, to assess how these strategies contribute to their adaptive capacity. METHODSWe combined sustainable rural livelihoods and livelihood resilience frameworks and operationalized them to: group farm households with similar livelihood strategies based on their income-generating activities; asses the influence of capital assets and context on the adoption of strategies; and relate these strategies with their performance in three dimensions of adaptive capacity, namely capacity for learning and adaptation, self-organization, and diversity. Information was gathered surveying a sample of 103 farm households. RESULTS AND CONCLUSIONSWe identified five livelihood strategies showing different degrees of adaptive capacity. Farm households either intensified production (21.3% of the sample) or pursued various diversification pathways based on additional off-farm work (28.6%), rural-tourism activities (22.7%), or added-value production (13.3%). Pensioners (11.8%) had a low endowment of assets and presented the lowest estimates in several dimensions of adaptive capacity. In contrast, diversification into rural tourism scored higher in adaptive capacity, showing greater proactive capacity, farmer organization, and multiple income sources. SIGNIFICANCEWe explored the multidimensional issues that influence and are influenced by the livelihood strategies and their adaptive capacity at the farm household level. Our work highlights the relevance of including income-generating activities in addition to structural, technical, and socioeconomic variables in characterizing farming systems. It demonstrates the role of farmer involvement in formal and informal social cooperation networks in the sustainability and adaptive capacity of their households. To be successful, diversification strategies may require certain prerequisites in the farms, while strategies based on off-farm activities, although they support improved financial performance of the farm household, could also contribute to the displacement of agriculture from mountain areas.

Physical Flood Vulnerability Assessment using Geospatial Indicator-Based Approach and Participatory Analytical Hierarchy Process: A Case Study in Kota Bharu, Malaysia

The most devastating flood event in Kota Bharu was recorded in December 2014, which affected several properties worth millions of dollars and thousands of homes. Damage to physical properties, especially buildings, is identified as a significant contributor to flood disasters in Malaysia. Therefore, it is essential to address physical flood vulnerability by developing an integrated approach for modeling buildings’ flood vulnerability to decrease the flood consequences. This study aims at developing a flood vulnerability assessment approach using an indicator-based model (IBM) for individual buildings in Kota Bahru, Kelantan, Malaysia. An intensive literature review and expert opinions were used to determine suitable indicators that contribute to the physical flood vulnerability of buildings. The indicators were grouped into three components, i.e., flood hazard intensity (I), building characteristics (C), and effect of the surrounding environment (E). The indicators were further refined based on expert opinions and Relative Importance Index (RII) analysis. Based on their contribution to the Malaysia local building flood vulnerability, priority weight is assigned by the experts to each of the selected indicators using the participatory Analytical Hierarchy Process (AHP). A spatial database of buildings in Kota Bharu is developed through field surveys and manually digitizing building footprints from satellite imageries. The identified indicators and their weight are added to each building footprint. The Weighted Linear Combination (WLC) aggregation method combined the weight of indicators into a vulnerability index and maps. The results of a physical flood vulnerability were validated using building damage information obtained through interviews with the community that experienced previous flood in the study area. The result showed that about 98% of the study area’s buildings have either moderate or low vulnerability to flooding. The flood vulnerability map has an overall accuracy of 75.12% and 0.63 kappa statistics. In conclusion, the IBM approach has been used successfully to develop a physical flood vulnerability for buildings in Kota Bharu. The model contributes to support different structural and non-structural approaches in the flood mitigations process.

A generic physical vulnerability model for floods: review and concept for data-scarce regions

Abstract. The use of different methods for physical flood vulnerability assessment has evolved over time, from traditional single-parameter stage–damage curves to multi-parameter approaches such as multivariate or indicator-based models. However, despite the extensive implementation of these models in flood risk assessment globally, a considerable gap remains in their applicability to data-scarce regions. Considering that these regions are mostly areas with a limited capacity to cope with disasters, there is an essential need for assessing the physical vulnerability of the built environment and contributing to an improvement of flood risk reduction. To close this gap, we propose linking approaches with reduced data requirements, such as vulnerability indicators (integrating major damage drivers) and damage grades (integrating frequently observed damage patterns). First, we present a review of current studies of physical vulnerability indicators and flood damage models comprised of stage–damage curves and the multivariate methods that have been applied to predict damage grades. Second, we propose a new conceptual framework for assessing the physical vulnerability of buildings exposed to flood hazards that has been specifically tailored for use in data-scarce regions. This framework is operationalized in three steps: (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking resulting index classes with damage patterns, utilizing a synthetic “what-if” analysis. The new framework is a first step for enhancing flood damage prediction to support risk reduction in data-scarce regions. It addresses selected gaps in the literature by extending the application of the vulnerability index for damage grade prediction through the use of a synthetic multi-parameter approach. The framework can be adapted to different data-scarce regions and allows for integrating possible modifications to damage drivers and damage grades.

Investigating the technical dimension of water supply resilience to disasters

Water supply system functionality is vital in the aftermath of disasters. Failure of water systems can not only cause difficulties for residents and critical users, but may also affect other hard and soft infrastructures and services. Using an indicator-based model, this paper focuses on identifying the technical factors that enable water system robustness to be measured, and proposes a metric for robustness quantification. The technical variables were gathered through a comprehensive literature review, then verified and ranked through a series of interviews with water supply and resilience specialists. The study identified vulnerability, redundancy, and criticality as the most significant technical factors affecting water supply system robustness, and consequently resilience. Utilising these indicators and their influence on public water supply resilience will enable local authorities to identify existing strengths and weaknesses, and to optimise investment to obtain the best results. In addition, the proposed metric reduces the complexity and multidimensionality of factors to a measure that will support optimisation by decision makers. As a case study, the proposed indicators were employed for a hypothetical scenario earthquake in Pukerua Bay, New Zealand to show how this metric can be used to measure system robustness.

The Management of Reputational Risks in Banks: Findings From Germany and Switzerland

This article identifies reputation-risk-relevant factors for banks, and the focus will be placed on the development of an indicator-based model for the assessment of reputation. Requirements and insights are based on a survey of credit institutions in Germany and Switzerland, which have been predominantly affected during the financial crisis by aptly nascent risks and which are thereby also partially affected even today. Reputation level can be considered as a temporally dynamical phenomenon which predominantly develops depending on the changes in the reputation drivers and expectations of the groups of stakeholders. This control parameter can be determined with the aid of Reputation Index Points (RIP). Efficient reputation risk management can, in the future, help prevent negative spillover effects from banks which face difficulties from the society or the taxpayers.

Analysis of Landslide Vulnerabilities in Small and Medium-Sized Cities of ColombiaⅡ: A Combined Analysis of Socioeconomic Vulnerability and Physical Vulnerability

Vulnerability assessments are essential for evaluating slope hazards and damages from natural disasters. The first part of this study focused on physical vulnerability assessments of small cities in Colombia. In this second part, we developed an indicator-based model for socioeconomic vulnerability assessment. We defined weights for indicators and proxy variables, including indicators for demographic and economic conditions, secondary-damage triggers, disaster preparedness, and response measures. Socioeconomic vulnerability scores were calculated and graded using the weights assigned to the proxy variables. In addition, the evaluation of the combined vulnerability to slope disasters incorporated the results of the physical vulnerability assessments that were conducted in the first part of this study. As a result, vulnerable areas similar to those identified in the field survey were obtained. In the future, these vulnerability assessment results will be used as basic input data for the forecasting and prevention of slope disasters in Colombia. Keywords: Landslide, Socio-economic Vulnerability, Index, Combined Vulnerability Assesment

Design of spatial PGIS-MCDA-based land assessment planning for identifying sustainable land-use adaptation priorities for climate change impacts

Climate change is an obvious worldwide phenomenon closely related to human development, growth and consumption patterns, and it threatens land use, development, people and the environment. Due to its characteristics, Spain is among the most vulnerable countries to climate change in the European Union (EU). Thus, spatial planning is considered one of the main instruments available to manage sustainable adaptation to climate change. This article presents an assessment framework for exploring climate change impacts using participatory geographic information systems (PGISs)-multi-criteria decision analysis (MCDA) spatial planning with the preference ranking organization method for enrichment of evaluations (PROMETHEE) in sustainable land-use adaptation. Assessment planning applies to any agroforestry system at a regional level for a municipality with higher vulnerability. An indicator-based model with five categorical values was developed to assess twelve possible impacts from climate change and the main threats of climate change to water sources, agriculture, soil, and land management. This model is available to manage sustainable land-use adaptation priorities for climate change in a spatial context. The model discusses the likelihood of implementing and adopting strategies for climate change adaptation as assessed by a sensitivity analysis and a professional online survey. Among the five strategies, scenario A (suitability map) accounts for 8.84% of the priority areas (v) and 2.13% of the hot spots (i) and was the scenario most supported by professionals, while scenario D (priority to socioeconomic) accounts for 3.07% of the priority areas and 10.12% of the hotspots, and the lowest number of professionals supported this scenario. The results summarize foreseeable problems derived from climate change effects that require urgent adaptation activities through spatial land assessment planning. Thus, this study provides some recommendations and limitations from which decision-makers can select the most suitable arrangement for an agroforestry system to make it climate-resilient, and the study is applicable to similar geographical and spatial locations.

Indicator-Based Geostatistical Models For Mapping Fish Survey Data

Marine research survey data on fish stocks often show a small proportion of very high-density values, as for many environmental data. This makes the estimation of second-order statistics, such as the variance and the variogram, non-robust. The high fish density values are generated by fish aggregative behaviour, which may vary greatly at small scale in time and space. The high values are thus imprecisely known, both in their spatial occurrence and order of magnitude. To map such data, three indicator-based geostatistical methods were considered, the top-cut model, min–max autocorrelation factors (MAF) of indicators, and multiple indicator kriging. In the top-cut and MAF approaches, the variable is decomposed into components and the most continuous ones (those corresponding to the low and medium values) are used to guide the mapping. The methods are proposed as alternatives to ordinary kriging when the variogram is difficult to estimate. The methods are detailed and applied on a spatial data set of anchovy densities derived from a typical fish stock acoustic survey performed in the Bay of Biscay, which show a few high-density values distributed in small spatial patches and also as solitary events. The model performances are analyzed by cross-validating the data and comparing the kriged maps. Results are compared to ordinary kriging as a base case. The top-cut model had the best cross-validation performance. The indicator-based models allowed mapping high-value areas with small spatial extent, in contrast to ordinary kriging. Practical guidelines for implementing the indicator-based methods are provided.

서울시의 도시특성을 고려한 사회경제적 재해 취약성 평가

This study is assessed social and economic vulnerability to natural disasters considering urban characteristics of Seoul. For this study, an indicator-based model, which is composed of 3 sub-indicators and 17 surrogate variables is applied. The 3 sub-indicators consist of demographic and social indicator, secondary damage caused indicator, and disaster preparedness and response indicator, and individual sub-indicators are made up of 6, 5, and 6 related surrogate variables. The spatial resolution of each surrogate variable is built in census output area as possible to reflect the local complexity of population density and wealth distribution. From the results of the application of the model, it can be confirmed that even if the same borough, a variety of vulnerabilities appear depending on the census output area-specific characteristics. Given vulnerability assessment result can be associated with the exposure information of a variety of natural disasters such as floods, landslides, earthquakes, and so on, and its result is expected to be utilized for prioritization of the disaster prevention projects characterized by the specific borough.

Framework to Assess City-scale Sustainability

This paper establishes the need to act at a city scale to reduce energy use and improve sustainability in general. The paper evaluates the plans that the City of Fargo (Climate zone 6), North Dakota has established to grow in a sustainable manner. As part of this effort, the City of Fargo is participating in the Georgetown University Energy Prize Competition to reduce the energy use of residential and municipal buildings. As such, the City is engaged in assessing, defining, designing and implementing the Go 2030 plan (2012) as the basis for a sustainable future. This paper reviews and evaluates several methods of city-scale assessments and identifies distinguishing characteristics of these tools and assessments and creates a categorization to make the methods ordered and understandable. The two broad categories developed are (1) category- and (2) indicator-based models of urban sustainability. Further, this paper reviews the tools through a literature search and compares the City of Fargo's Go 2030 plan against these categorizations. Based on this evaluation this paper proposes a framework that has the potential to fill the gaps in the current Go2030 plan.

The INDICARE-model – measuring and caring about participation in higher education's sustainability assessment

The implementation of sustainability in higher education has been advanced over at least the last two decades and brought sustainability assessment on the research agenda of Education for Sustainable Development (ESD) and sustainability science. Participatory approaches have gained increasing attention in these endeavours, but remain often vague and less addressed in sustainability assessment procedures. To fill in this gap, an indicator-based model, INDICARE, was developed that can assist in assessing participatory processes within higher education's sustainability initiatives. The objective of this paper is to introduce and discuss the model's theoretical background, its structure, applicability, and how it can broaden the perspectives on participation and sustainability assessment in the university context.Embedded in a cross-sectional qualitative research design, the model was developed in iterative stages and was presented and adjusted along six feedback loops, having been presented to 98 persons during conferences, workshops and university meetings. Inspired by biophilic ideas, transformative learning theories and participatory evaluation, INDICARE follows an ecocentric and integrative perspective that places the earth and its community at the centre of attention. A preliminary set of thirty indicators and practices, grouped in three categories of context, process, and transformation, is proposed. The assessment process itself is considered as a thought-provoking exercise rather than as a control tool and emphasizes the interplay of personal reflection and action-oriented outreach. INDICARE intends to invigorate the sustainability debate in higher education, in particular by proposing a more holistic approach to assessment that underlines experiencing the interconnectedness of human–nature relationships, combined with reflective exercises that can respond better to the call for transformation on individual and institutional level.

Operational Models for Improving the Targeting Efficiency of Development Policies: A Systematic Comparison of Different Estimation Methods Using Out-of-sample Tests

There is a long-standing belief that accurate targeting of the poor can improve the effectiveness of development policy. While a number of factors, such as governance failure, inadequate targeting methods, political interference or lack of political will, might explain low targeting efficiency, this article focuses on improving indicator-based models that identify poor households more accurately. Using stepwise regressions along with out-of-sample validation tests, this article develops proxy means test models for rural and urban Malawi. The models developed have proved their validity and can therefore be used to target a wide range of development policies in the country. Likewise, the research here can be extended to other developing countries.

Soil compaction—indicators for the assessment of harmful changes to the soil in the context of the German Federal Soil Protection Act

Soil compaction in agriculture induced by large-scale equipment is of growing concern. Heavy wheel loads used in arable cropping have the potential to cause irreversible damage to the subsoil structure and may lead to harmful soil compaction. In order to sustain or improve soil health or fitness on a sustainable basis, indicators are needed to assess the changes in the soil structure and the respective soil functions. This requires an adequate verification of methods for distinguishing between the impairment of soil structure and the disruption of soil functions, and for the respective subject of protection. In this article the link between existing models of soil physical prognosis, practical guidelines, and criteria for the identification of affected soil structure is demonstrated, and a viable concept to distinguish harmful changes to the soil is presented. The concept consists of methods for soil physical analysis such as “ pre-compression stress” and “ loading ratio”, practical recommendations for best management practice, and an indicator-based model for the identification of harmful subsoil compaction derived from a research project for the German Federal Environmental Agency, making it possible to determine for a respective location the required level of action far beyond the common practice of precautions against harmful soil compaction.

Spatio-Temporal Autoregressive Models of Neighborhood Effects

Using 70,822 observations on housing prices during 1969-91 from Fairfax County Virginia, this manuscript demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatio temporal autoregression with 12 variables reduced median absolute error by 37.35% relative to an indicator-based model with 26 variables. One-step ahead forecasts also document the improved performance of the proposed spatio-temporal model. In addition, the manuscript illustrates techniques for rapidly computing the estimates and shows how to compute indices for any location.