A diagnostic modelling framework to construct indices of biotic integrity: A case study of fish in the Zeeschelde estuary (Belgium)

2011 ◽  
Vol 94 (3) ◽  
pp. 222-233 ◽  
Author(s):  
Paul Quataert ◽  
Pieter Verschelde ◽  
Jan Breine ◽  
Geert Verbeke ◽  
Els Goetghebeur ◽  
...  
Keyword(s):  
Biotic Integrity ◽  
Modelling Framework

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

scholarly journals The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

2019 ◽  
Vol 25 (3) ◽  
pp. 476-498 ◽  
Author(s):  
Omogbai Oleghe ◽  
Konstantinos Salonitis
Keyword(s):  
Discrete Event Simulation ◽  
Hybrid Model ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Hybrid Modelling ◽  
Content Type ◽  
Event Simulation ◽  
Modelling Framework ◽  
Field Notes

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty.

Can recreational ecosystem services be inferred by integrating non-parametric scale estimators within a modelling framework? The birdwatching potential index as a case study

2019 ◽  
Vol 103 ◽  
pp. 395-409 ◽  
Author(s):  
Mário Santos ◽  
Diogo Carvalho ◽  
Antonio Luis ◽  
Rita Bastos ◽  
Samantha Jane Hughes ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Modelling Framework ◽  
Potential Index ◽  
Non Parametric

MULTI–STATE ANALYSIS OF PROCESS STATUS USING MULTILEVEL FLOW MODELLING AND BAYESIAN NETWORK

2016 ◽  
Vol 78 (8-3) ◽  
Author(s):  
Mohamed A. R. Khalil ◽  
Arshad Ahmad ◽  
Tuan Amran Tuan Abdullah ◽  
Ali Al-Shatri ◽  
Ali Al-Shanini
Keyword(s):  
Bayesian Network ◽  
Fault Detection And Diagnosis ◽  
Success Rates ◽  
Probabilistic Information ◽  
Modelling Framework ◽  
Predicting Success ◽  
Detection And Diagnosis ◽  
Probability Information ◽  
Multilevel Flow Modeling

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method.

Assessment of calanchi and rill-interrill erosion susceptibility in northern Liguria, Italy: A case study using a probabilistic modelling framework

Geoderma ◽  
2020 ◽  
Vol 371 ◽  
pp. 114367 ◽  
Author(s):  
Michael Maerker ◽  
Alberto Bosino ◽  
Claudia Scopesi ◽  
Paolo Giordani ◽  
Marco Firpo ◽  
...  
Keyword(s):  
Probabilistic Modelling ◽  
Modelling Framework ◽  
Interrill Erosion

Understanding climate-change adaptation on Kakadu National Park, using a combined diagnostic and modelling framework: a case study at Yellow Water wetland

2018 ◽  
Vol 69 (7) ◽  
pp. 1146 ◽  
Author(s):  
Leo X. C. Dutra ◽  
Peter Bayliss ◽  
Sandra McGregor ◽  
Peter Christophersen ◽  
Kelly Scheepers ◽  
...  
Keyword(s):  
National Park ◽  
Aboriginal People ◽  
Bayesian Belief Networks ◽  
Freshwater Ecosystems ◽  
Social Ecological Systems ◽  
Modelling Framework ◽  
Cultural Sites ◽  
Governance Systems ◽  
Diagnostic Framework

This paper describes a semi-quantitative approach for the assessment of sea-level rise (SLR) impacts on social–ecological systems (SES), using Yellow Water wetland on Kakadu National Park as a case study. The approach includes the application of a diagnostic framework to portray the existing SES configuration, including governance structures, in combination with qualitative modelling and Bayesian belief networks. Although SLR is predicted to cause saltwater inundation of freshwater ecosystems, cultural sites and built infrastructure, our study suggested that it may provide also an opportunity to bring together Indigenous and non-Indigenous knowledge and governance systems, towards a commonly perceived threat. Where feasible, mitigation actions such as levees may be required to manage local SLR impacts to protect important freshwater values. In contrast, adaptation will require strategies that facilitate participation by Kakadu Bininj (the Aboriginal people of Kakadu National Park) in research and monitoring programs that enhance understanding of salinity impacts and the adaptive capacity to respond to reasonably rapid, profound and irreversible future landscape-scale changes.

scholarly journals Land-use and related pressures have reduced biotic integrity more on islands than on mainlands

2019 ◽  
Author(s):  
Katia Sanchez-Ortiz ◽  
Ricardo E. Gonzalez ◽  
Adriana De Palma ◽  
Tim Newbold ◽  
Samantha L. L. Hill ◽  
...  
Keyword(s):  
Native Species ◽  
Oceanic Islands ◽  
Pressure Effects ◽  
Biotic Integrity ◽  
Biodiversity Hotspots ◽  
Global Models ◽  
Modelling Framework ◽  
Road Development ◽  
Safe Limits ◽  
Disproportionate Number

ABSTRACTTracking progress towards biodiversity targets requires indicators that are sensitive to changes at policy-relevant scales, can easily be aggregated to any spatial scale and are simple to understand. The Biodiversity Intactness Index (BII), which estimates the average abundance of a diverse set of organisms in a given area relative to their reference populations, was proposed in 2005 in response to this need. A new implementation of BII was developed as part of the PREDICTS project in 2016 and has been adopted by GEO BON, IPBES and CBD. The previous global models for BII estimation could not account for pressures having different effects in different settings. Islands are a setting of particular interest: many are home to a disproportionate number of endemic species; oceanic islands may have relatively low overall species diversity because of their isolation; and the pattern and timing of human pressures can be very different from that seen on mainlands. Here, we test whether biotic integrity – as estimated by BII – has decreased more severely on islands than mainlands. We update methods previously used to estimate BII globally (Newbold et al., 2016) to allow pressure effects to differ between islands and mainlands, while also implementing some other recent improvements in modelling. We estimate BII for islands and mainlands by combining global models of how two aspects of biodiversity – overall abundance, and compositional similarity to minimally-impacted sites – have been affected by human pressures. We use these models to project high-resolution (∼1km2) global maps of BII for the year 2005. We calculate average BII for island and mainland biomes, countries, IPBES regions and biodiversity hotspots; and repeat our analyses using a richness-based version of BII. BII on both islands and mainlands has fallen below the values proposed as safe limits across most regions, biomes and biodiversity hotspots. Our BII estimates are lower than those published in 2016, globally, within all biodiversity hotspots and within most biomes, and show greater spatial heterogeneity; detailed analysis of these differences shows that they arise mostly from a combination of improvements to the modelling framework. Average BII does not strongly differ between islands and mainlands, but richness-based BII has fallen by more on islands. It seems native species are more negatively affected by rising human population density and road development on islands than mainlands, and islands have seen more land conversion. Our results highlight the parlous state of biodiversity native to islands.

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