scholarly journals A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil

2021 ◽  
Vol 13 (20) ◽  
pp. 11267
Author(s):  
Afshin Ghahramani ◽  
John McLean Bennett ◽  
Aram Ali ◽  
Kathryn Reardon-Smith ◽  
Glenn Dale ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Expert Knowledge ◽  
Model Performance ◽  
Ground Cover ◽  
Bayesian Belief Network ◽  
Data Availability ◽  
Network Modelling ◽  
Practical Result ◽  
Belief Network ◽  
Active Coal

Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand.

Structuring a Bayesian belief network using expert knowledge for landslide hazard assessment

2021 ◽  
Author(s):  
Shreyasi Choudhury ◽  
Bruce D. Malamud ◽  
Amy Donovan
Keyword(s):  
Hazard Assessment ◽  
Expert Knowledge ◽  
Grey Literature ◽  
Slope Angle ◽  
Bayesian Belief Network ◽  
Landslide Hazard ◽  
Small Scale ◽  
Landslide Hazard Assessment ◽  
Belief Network ◽  
Darjeeling Himalayas

<p>Landslide hazard assessment in India using historical data faces three challenges: (i) difficulty of obtaining systematic landslide occurrence data; (ii) under-representation of small-scale landslides; (iii) lack of recording of the physical/anthropogenic influences on landsliding. Here we show development of a Bayesian Belief Network (BBN) for a multi-hazard landslide assessment using experts’ judgements. Experts were chosen based on their experience on landslides and/or in Darjeeling Himalayas. A BBN produces a probability estimation of possible events and is a graph containing a set of variables (nodes) and conditional (in)dependencies between the nodes (arcs).</p><p>To better understand the relative weighting of potential causes of landslides in our case study area -Darjeeling Himalayas- we carried out four steps. (<strong>Step 1</strong>) We reviewed 29 peer- and grey-literature sources to list 13 physical/anthropogenic variables that might influence landsliding. (<strong>Step 2</strong>) We interviewed 11 experts about the importance of these 13 variables and asked for additional potential variables (resulting in 35 variables). (<strong>Step 3</strong>) We used interviews plus questionnaire to ask 16 experts to rate each of the 35 variables (scale 1-10) as to their potential to influence landsliding. The experts also added 7 more variables (resulting in 46 variables). (<strong>Step 4</strong>) Based on the ratings and interviews, we chose 35 out of 46 variables as our BBN nodes and from these the BBN arcs. Examples of these variables include rainfall, wildfires, geological weathering, planned infrastructure loading, cultivation (planned/unplanned), railway/road construction changing slope angle (planned), relief, slope, soil cohesion. Based on this study, we found that judgement of local people/academicians/technical experts can be of help whilst developing a BBN structure, allowing us to calculate probabilistic relationships between the nodes in a BBN. This process, therefore, can be utilised for landslide-based multi-hazard assessment in low data regions.</p>

The belief index: An empirical measure for evaluating outcomes in Bayesian belief network modelling

2012 ◽  
Vol 228 ◽  
pp. 123-129 ◽  
Author(s):  
L. Vilizzi ◽  
A. Price ◽  
L. Beesley ◽  
B. Gawne ◽  
A.J. King ◽  
...  
Keyword(s):  
Bayesian Belief Network ◽  
Empirical Measure ◽  
Network Modelling ◽  
Belief Network

Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater

2017 ◽  
Vol 109 ◽  
pp. 144-154 ◽  
Author(s):  
Guido Carvajal ◽  
David J. Roser ◽  
Scott A. Sisson ◽  
Alexandra Keegan ◽  
Stuart J. Khan
Keyword(s):  
Municipal Wastewater ◽  
Bayesian Belief Network ◽  
Network Modelling ◽  
Belief Network ◽  
Chlorine Disinfection ◽  
Pathogenic Viruses

scholarly journals Increasing Local Fish Consumption: A Bayesian Belief Network Analysis

2021 ◽  
Vol 33 (1) ◽  
pp. 104-121
Author(s):  
Samantha Paredes ◽  
Sean Pascoe ◽  
Louisa Coglan ◽  
Carol Richards
Keyword(s):  
Network Analysis ◽  
Fish Consumption ◽  
Bayesian Belief Network ◽  
Belief Network

scholarly journals Operational USLE-Based Modelling of Soil Erosion in Czech Republic, Austria, and Bavaria—Differences in Model Adaptation, Parametrization, and Data Availability

2020 ◽  
Vol 10 (10) ◽  
pp. 3647
Author(s):  
Peter Fiener ◽  
Tomáš Dostál ◽  
Josef Krása ◽  
Elmar Schmaltz ◽  
Peter Strauss ◽  
...  
Keyword(s):  
Czech Republic ◽  
Soil Erosion ◽  
Water Conservation ◽  
Data Availability ◽  
The European Union ◽  
Specific Knowledge ◽  
The Czech Republic ◽  
Environmental Threats ◽  
Implementation Techniques ◽  
Consistent Method

In the European Union, soil erosion is identified as one of the main environmental threats, addressed with a variety of rules and regulations for soil and water conservation. The by far most often officially used tool to determine soil erosion is the Universal Soil Loss Equation (USLE) and its regional adaptions. The aim of this study is to use three different regional USLE-based approaches in three different test catchments in the Czech Republic, Germany, and Austria to determine differences in model results and compare these with the revised USLE-base European soil erosion map. The different regional model adaptations and implementation techniques result in substantial differences in test catchment specific mean erosion (up to 75% difference). Much more pronounced differences were modelled for individual fields. The comparison of the region-specific USLE approaches with the revised USLE-base European erosion map underlines the problems and limitations of harmonization procedures. The EU map limits the range of modelled erosion and overall shows a substantially lower mean erosion compared to all region-specific approaches. In general, the results indicate that even if many EU countries use USLE technology as basis for soil conservation planning, a truly consistent method does not exist, and more efforts are needed to homogenize the different methods without losing the USLE-specific knowledge developed in the different regions over the last decades.

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