Risk-based Decision Research Articles

Recalibration Methods for Improved Clinical Utility of Risk Scores.

BackgroundAn established risk model may demonstrate miscalibration, meaning predicted risks do not accurately capture event rates. In some instances, investigators can identify and address the cause of miscalibration. In other circumstances, it may be appropriate to recalibrate the risk model. Existing recalibration methods do not account for settings in which the risk score will be used for risk-based clinical decision making.MethodsWe propose 2 new methods for risk model recalibration when the intended purpose of the risk model is to prescribe an intervention to high-risk individuals. Our measure of risk model clinical utility is standardized net benefit. The first method is a weighted strategy that prioritizes good calibration at or around the critical risk threshold. The second method uses constrained optimization to produce a recalibrated risk model with maximum possible net benefit, thereby prioritizing good calibration around the critical risk threshold. We also propose a graphical tool for assessing the potential for recalibration to improve the net benefit of a risk model. We illustrate these methods by recalibrating the American College of Cardiology (ACC)–American Heart Association (AHA) atherosclerotic cardiovascular disease (ASCVD) risk score within the Multi-Ethnic Study of Atherosclerosis (MESA) cohort.ResultsNew methods are implemented in the R package ClinicalUtilityRecal. Recalibrating the ACC-AHA-ASCVD risk score for a MESA subcohort results in higher estimated net benefit using the proposed methods compared with existing methods, with improved calibration in the most clinically impactful regions of risk.ConclusionThe proposed methods target good calibration for critical risks and can improve the net benefit of a risk model. We recommend constrained optimization when the risk model net benefit is paramount. The weighted approach can be considered when good calibration over an interval of risks is important.

MA07.02 Evaluating Circulating Tumor DNA to Predict Overall Survival Risk in Non-Squamous Non-Small Cell Lung Cancer in IMpower150

A surrogate endpoint for overall survival (OS) could inform risk-based treatment decisions and provide early signals of efficacy to accelerate drug development for non-small cell lung cancer (NSCLC). We evaluated the utility of longitudinal circulating tumor (ct)DNA testing to predict OS and progression-free survival (PFS) in IMpower150, a phase III randomized controlled study of first-line (1L) chemotherapy plus atezolizumab and/or bevacizumab in patients with metastatic non-squamous NSCLC.

Risk-Based Wellhead Protection Decision Support: A Repeatable Workflow Approach.

Environmental water management often benefits from a risk-based approach where information on the area of interest is characterized, assembled, and incorporated into a decision model considering uncertainty. This includes prior information from literature, field measurements, professional interpretation, and data assimilation resulting in a decision tool with a posterior uncertainty assessment accounting for prior understanding and what is learned through model development and data assimilation. Model construction and data assimilation are time consuming and prone to errors, which motivates a repeatable workflow where revisions resulting from new interpretations or discovery of errors can be addressed and the analyses repeated efficiently and rigorously. In this work, motivated by the real world application of delineating risk-based (probabilistic) sources of water to supply wells in a humid temperate climate, a scripted workflow was generated for groundwater model construction, data assimilation, particle-tracking and post-processing. The workflow leverages existing datasets describing hydrogeology, hydrography, water use, recharge, and lateral boundaries. These specific data are available in the United States but the tools can be applied to similar datasets worldwide. The workflow builds the model, performs ensemble-based history matching, and uses a posterior Monte Carlo approach to provide probabilistic capture zones describing source water to wells in a risk-based framework. The water managers can then select areas of varying levels of protection based on their tolerance for risk of potential wrongness of the underlying models. All the tools in this workflow are open-source and free, which facilitates testing of this repeatable and transparent approach to other environmental problems.

Framework for Risk-Based Decision Support on Infiltration and Inflow to Wastewater Systems

Infiltration and inflow (I/I) to wastewater systems cause e.g., flooding, pollution, and the unnecessary use of the limited resources in society. Due to climate change and an increased need for the renewal of piping systems, making the right decisions on how to handle I/I is more important than ever. This paper presents a novel framework for risk-based decision support on I/I based on established theories on risk assessment and decision-making. The framework is presented on a general level and suggests that uncertainties are included in the decision-making process, together with criteria representing the economic, social, and environmental dimensions of sustainability. Published models on I/I and decision support are evaluated based on criteria from the framework showing that (1) the models rarely include risk-based decision-making or uncertainties in the analyses and that (2) most models only include project-internal financial aspects, excluding social and environmental, as well as project-external aspects, of I/I and I/I measures. A need for further research to develop a more holistic decision support model for I/I is identified, and it is concluded that the application of the proposed framework can contribute to more sustainable decisions on how to handle I/I and provide transparency to the process.

Gaussian process machine learning and Kriging for groundwater salinity interpolation

Gaussian processes (GPs) provide statistically optimal predictions in the sense of unbiasedness and maximal precision. Although the modern implementation of GPs as a machine learning technique is more capable and flexible than Kriging, their employment in environmental science is less routine. Their flexibility and capability as a spatial data interpolation technique are demonstrated by applying them to groundwater salinity prediction in a data-sparse region in Australia. By learning from multiple data sources, including AEM and DEM data, GPs have generated groundwater salinity maps with rich local details and quantified uncertainty to support risk-based decision making. The results demonstrate the great worth of nonpoint data with regional spatial coverage to provide more realistic heterogeneity in aquifer properties that are critical for many studies such as contaminant transport. GPs should be further encouraged in groundwater science for data interpolation and prediction, especially when point measurements are sparse and multiple predictors are available.

Risk based triage for complex surgery in ovarian cancer: ready for prime time

<h3>Objectives:</h3> We developed a triage algorithm (Figure) that identifies patients who are at highest risk of severe morbidity/mortality (M/M) after cytoreductive surgery for advanced ovarian cancer (OC). We previously validated our algorithm in a separate internal validation cohort as well as a low complexity national dataset (National Surgical Quality Improvement Program database). We wanted to test the validity of the algorithm in an international high complexity surgical setting. <h3>Methods:</h3> We included patients who underwent cytoreductive surgery in the primary (PDS) or interval (IDS) setting for stage IIIC/IV OC at a single institution between 10/2011 and 11/2019. This cohort included SCORPION trial patients until 5/2016 and non-trial patients thereafter. The data was prospectively obtained. Surgical complexity was classified as low, intermediate and high using the Aletti Score as previously described. Using the algorithm we retrospectively classified patients as ‘high risk' or ‘triage appropriate' and compared outcomes (30-day Accordion grade 3+ complications, 90-day mortality) between the two groups using the chi-square test or Fisher's exact test. <h3>Results:</h3> We included 625 patients; the mean age was 58.7 years, 73.6% were stage IIIC, 63.0% underwent PDS and 21.0% (131/625) were classified as high risk. Surgical complexity was intermediate or high in 82.6% of patients (95.7% of PDS patients and 60.2% of IDS patients) and mean operative time was 377 (SD, 140) minutes. We observed that high risk patients i) had a 3-fold higher rate of 90-day mortality (6.1% vs 2.0%, p=0.03), ii) were more likely to experience 90-day mortality following an Accordion grade 3+ complication (25.9% vs. 10.0%, p=0.05), and iii) had comparable rates of Accordion grade 3+ complications (20.6% vs 16.2%) when compared to triage appropriate patients. Rates of complete cytoreduction were 72.5% and 80.5% (p=0.05) for high risk and triage appropriate patients, respectively. <h3>Conclusions:</h3> Use of our evidence-based triage algorithm identifies patients at very high risk of surgical M/M after high complexity debulking surgery. These patients are not ideal candidates for surgery when a high complexity operation is anticipated. Given the validation of the algorithm in varied settings, risk-based decision making should be standard of care when considering cytoreductive surgery for patients with advanced OC.

Shifts in Medial Orbitofrontal Cortex Activity from Adolescence to Adulthood.

Adolescents are characterized by a propensity for risky and impulsive behaviors, likely due to immature frontostriatal circuits. The medial orbitofrontal cortex (MO) is linked to risk and reward prediction during decision-making. Identifying age-dependent differences in MO activity and its inputs to downstream regions can elucidate the neural substrates that permit the transition from high-risk adolescent behaviors to increased risk assessment in adulthood. Action selection biased by information gathered by the MO is likely carried out by efferents into the nucleus accumbens (NAc), which guides reward-directed behaviors. Despite the large age dependency of risk-based decision-making, there is nothing known about adolescent MO activity. Here, we recorded action potentials of MO neurons from anesthetized adult and adolescent rats in vivo. On average, adolescent MO neurons fire faster and within narrower ranges than adults, and adolescents have more active MO neurons than adults. Using antidromic stimulation of axon terminals to identify MO neurons that project to NAc (MO→NAc), we found that adolescent MO→NAc neurons have a narrower range of firing frequencies than non-NAc-projecting MO neurons and adult MO→NAc neurons. These age-dependent differences in MO and MO→NAc populations may result from the fine-tuning of circuits between adolescence and adulthood that promote specific age-dependent behaviors.

Incorporating weather forecasts into risk-based irrigation decision-making

ABSTRACT Irrigation decision-making is complex. It requires balancing multiple risks . We examined if incorporation of weather forecasts into irrigation decision-making improved environmental and economic outcomes on dairy farms in Canterbury, New Zealand. We focused on understanding how farmers can manage irrigation decision-making with uncertain weather forecasts, and the associated impacts on drainage, pasture growth and economics. Previous work had demonstrated that weather conditions were not used in irrigation decision-making as much as desirable and that weather forecast uncertainty was not explicitly considered. , We incorporated weather forecasts as a perfect forecast and the best available forecast. We used probability percentiles to incorporate forecast uncertainty into decision-making. The research hypothesis was that through utilising risk profiles of factors that influence environmental and economic outcomes of irrigation decisions, farmers could improve decison-makingwhen faced with uncertain future weather states. When weather forecasts were integrated into irrigation decision-making, there were environmental and economic benefits relative to a simple deficit-based irrigation decision rule. Based on a case study, we predicted 5% additional profit (approximately, $200 per hectare) may be possible when the best available forecasts was used relative to a simple deficit-based decision rule, and a further 1% profit if forecast uncertainty was considered.

Risk-based multi-criteria decision analysis of gas power plants placement in semi-arid regions

The purpose of this research is to determine the optimum location to construct gas power plants (GPPs) in semi-arid regions. A combination of ordered weight averaging (OWA) and analytic hierarchy process (AHP) method named OWA-AHP is utilized to prepare land suitability maps with different risk scenarios when fuzzy method is to homogenize inputs. In the proposed method, AHP is to weigh each different parameter while OWA considers risk levels. In order to validate the accuracy of the proposed method, the receiver operating characteristics (ROC) curve is investigated. Besides, the self-organizing map (SOM) algorithm and Pearson correlation are used to determine the most important parameters for constructing GPP. According to obtained results, the areas located in the north and parts of the east and south of the selected case study (about 15%) at all risk levels are the optimum areas to be hosted for GPPs. Furthermore, ROC curve shows that the Area Under the Curve (AUC) values are high for both AHP and OWA-AHP methods (AUC Fuzzy-AHP = 94.0%, AUC OWA = 89.0%). The results of the SOM algorithm and Pearson correlation with high accuracy (RModel1: 0.853 and RModel2: 0.940) also depict that distance to the pipeline and road are the most important parameters to identify suitable locations for GPP.

Consequence modelling for Arctic ship evacuations using expert knowledge

Risk-based decision-making is central to the development of Arctic shipping policy and regulation. Policy-makers within the International Maritime Organization rely on the Formal Safety Assessment (FSA) methodology to evaluate proposed regulatory changes and Arctic ship operators rely on it to establish operating limits and procedures. The FSA recommends incorporating life-safety consequence in the assessment of maritime industry risk. This paper presents an expert-based assessment of the factors that influence the potential for loss of life during an Arctic ship evacuation and quantified consequence severities for a range of evacuation scenarios. A two-phased mixed methods design is used to elicit expert knowledge. Sixteen experts in the fields of Arctic seafaring, policy and regulation, academia and research, and ship design participated in the study. Semi-structured interviews elicited perspectives on the factors that influence the expected number of fatalities resulting from an evacuation in Arctic waters. Surveys were administered in which evacuation scenarios were rated for the level of life-safety consequence severity they pose. This study provides a scenario-based life-safety consequence model for Arctic ship evacuations. Results show evacuation of passenger vessels poses the highest consequence severity of evaluated ship types. Response time and the time available to evacuate have the greatest levels of influence on consequence severity. Implications for Arctic marine policy include the need for enhanced competency and training for Arctic ship crews and SAR services, continued research and development of Arctic life-saving appliances to satisfy Polar Code functional requirements, heightened regulatory oversight of Arctic cruise operations, and consideration of inclusion of fishing vessels under the Polar Code. Application of the results to the FSA methodology is discussed.

Application of risk-based fuzzy decision support systems in new product development: An R-VIKOR approach

Innovative manufacturing firms strive to sustain and enhance their competitive advantages by running a range of new product development (NPD) projects in a consistent manner. The capital and time required to execute the NPD projects have substantially increased over the past years. This magnified the risk-aversion behavior of R&D managers and has increased their sensitivity towards the underlying risk of NPD projects. In particular, the R&D departments have recently started to proactively assess the accuracy of ambiguous information that is extensively used in preliminary market study and customer requirements analysis. Thanks to its high performance in dynamic environments, the R-numbers method can be employed to capture and analyze the risk of fuzzy numbers in a variety of decision making models. To tackle the complexity of such analysis, this paper proposes a novel risk-based fuzzy VIKOR (R-VIKOR) methodology. Using the interpretive structural modeling, the risk factors are first classified to identify and rank the existing critical risk factors of NPD projects. The ultimate goal of this study is to develop a practical yet simple decision support system tool that enables the R&D managers to effectively examine the riskiness of fuzzy information and assess the relevant risk factors. A real-world case study is presented to test and examine the accuracy and effectiveness of the proposed risk management method.

Development of a Probabilistic Framework for Risk-Based Well Decommissioning Design

Summary In the next decades, tens of thousands of well plugging and abandonment (P&amp;A) operations are expected to be executed worldwide. Decommissioning activities in the North Sea alone are forecasted to require 2,624 wells to be plugged and abandoned during the 10-year period starting from 2019 (Oil&amp;Gas_UK 2019). This increase in decommissioning activity level and the associated high costs of permanent P&amp;A operations require new, fit-for-purpose, P&amp;A design tools and operational technologies to ensure safe and cost-effective decommissioning of hydrocarbon production wells. This paper introduces a novel modeling framework to support risk-based evaluation of well P&amp;A designs using a fluid-flow simulation methodology combined with probabilistic estimation techniques. The developed well-centric modeling framework covers the full range of North Sea P&amp;A well designs and allows for quantification of the long-term (thousands of years) evolution of hydrocarbon movement in the plugged and abandoned well. The framework is complemented by an in-house visualization tool for identification of the dominant hydrocarbon flow-paths. Monte Carlo methods are used to account for uncertainties in the modeling inputs, allowing for robust comparison of various P&amp;A design options, which can be ranked on the basis of hydrocarbon leakage risks. The proposed framework is able to model transient conditions within the well P&amp;A system, allowing for the development of new key performance indicators (e.g., time until hydrocarbons reach surface and changes in hydrocarbon saturation within the P&amp;A well). Such key performance indicators are not commonly used, because most published work in this area relies on steady-state P&amp;A models. The developed framework was used in the assessment of several P&amp;A design cases. The results obtained, which are presented in this paper, demonstrate its value for supporting risk-based decision-making by allowing for quantitative comparison of the expected performance of multiple P&amp;A design options for given well/reservoir conditions. The framework can be used for identifying cost-effective, fit-for-purpose P&amp;A designs, for example by optimizing the number, location, and length of wellbore barriers and evaluating the effectiveness of annular cement sheath remedial operations. Additionally, this framework can be used as a sensitivity analysis tool to identify the critical parameters that have the greatest impact on the modeled leakage risks, to guide data acquisition plans and model refinement steps aimed at reducing the uncertainties in key model parameters.

Risk-Based Warning Decision Making of Cascade Breaching of the Tangjiashan Landslide Dam and Two Smaller Downstream Landslide Dams

Mega earthquakes or serious rainfall storms often cause crowded landslides in mountainous areas. A large part of these landslides are very likely blocking rivers and forming landslide dams in series along rivers. The risks of cascading failure of landslide dams are significantly different from that of a single dam. This paper presented the work on risk-based warning decision making on cascading breaching of the 2008 Tangjiashan landslide dam and two small downstream landslide dams in a series along Tongkou River. The optimal decision was made by achieving minimal expected total loss. Cascade breaching of a series of landslide dams is more likely to produce a multi-peak flood. When the coming of the breaching flood from the upstream dam perfectly overlaps with the dam breaching flood of the downstream dam, a higher overlapped peak flood would occur. When overlapped peak flood occurs, the flood risk would be larger and evacuation warning needs to be issued earlier to avoid serious life loss and flood damages. When multi-peak flood occurs, people may be misled by the warning of the previous peak flood and suddenly attacked by the peak flood thereafter, incurring catastrophic loss. Systematical decision making needs to be conducted to sufficiently concern the risk caused by each peak of the breaching flood. The dam failure probabilityPflinearly influences the expected life loss and flood damage but does not influence the evacuation cost. The expected total loss significantly decreases withPfwhen the warning time was insufficient. However, it would not change much withPfwhen warning time is sufficient.

Risk-based decision framework based on R-numbers and best-worst method and its application to research and development project selection

Presently, fierce market competition has forced companies to invest in targeted research and development (R&D) projects in order to appropriately allocate resources and achieve promising results. For companies to develop, it is crucial that they incorporate the uncertainty and risk factors inherent in R&D project selection into the decision-making framework to obtain reliable outputs. The R-numbers methodology is an innovative model for analyzing the effects of uncertainty and risk. However, relatively few studies have incorporated R-numbers into decision-making problems, especially when the available data are in the form of pairwise comparisons. Nevertheless, the best–worst method (BWM) is a practical multi-criteria framework with pairwise relations. Thus, this study investigated a novel risk-based decision model called “R-BWM” that extended an additive BWM based on R-numbers. The proposed approach is applied to address the uncertainty and risk in the R&D project selection for a medical device company. The results show the ability of the proposed R-BWM to enhance the performance of the classical BWM through capturing data deviations and providing more robust results.

Comparative angler catch rates of native versus alien piscivorous fish in an invaded river fishery

Recreational angling is a major introduction pathway for large-bodied alien fishes into freshwaters, where the fish are released to enhance angling success and increase angler satisfaction, despite this often resulting in invasive populations. There is a thus a need to understand the role of these alien species in angler catches to enable more informed risk-based decisions to be made on future releases. In England, the invasive, piscivorous pikeperch Sander lucioperca has been present in rivers since the 1960s. Anglers target invasive pikeperch in fisheries where the native piscivorous pike Esox lucius is also present; this includes two rivers in the lower Severn basin, western England (main River Severn and Warwickshire Avon). To assess the contributions of invasive pikeperch to angler catches, the aim here was to compare their catches with those of native pike in the lower Severn basin in relation to angling effort and methods, abiotic conditions, and fish size. In 307 angling sessions across 16 anglers where at least one fish was captured, 428 pike and 266 pikeperch were captured. In a sub-set of data from six anglers who submitted catch returns that included non-capture sessions, 78 % of sessions resulted in the capture of at least one pike or pikeperch. Catch rates of pike were significantly higher than pikeperch in the main River Severn but not the Avon. Captured pike were significantly larger than pikeperch, but pikeperch were larger relative to the maximum size each species reaches in England. Lures generally captured more pike than any other method, with these fish tending to be smaller than those caught on other methods; these patterns were not evident in pikeperch. Both species were captured across a broad range of river flow conditions (Q6 to Q99). Only 19 % of successful angling sessions resulted in the capture of both species, suggesting some species selectivity by anglers. These results emphasise that alien fish species can provide important angling resources in recreational fisheries, although management decisions on future introductions should still consider their ecological risks.

Stochastic programming for floodwater utilization of a complex multi-reservoir system considering risk constraints

Floodwater utilization through dynamic operating flood limited water levels (FLWLs) of reservoirs plays an important role in flood control and water conservation. This study proposes a risk-based aggregation-decomposition method for floodwater utilization of a complex multi-reservoir system. The original system is decomposed into four basic configurations. For the subsystem with hydraulic connections, the upper bound of FLWL of each reservoir is identified based on a given risk level for the subsystem and risk hedging between reservoirs. A stochastic programming model to maximize hydropower generation is then established for joint operation of the multi-reservoir system to find and update the optimal operation strategy during flood seasons. The mixed cascade reservoirs in the Pi River Basin are selected as a case study. The application results are compared with those of the original design model and deterministic operation model without considering risk, which shows that joint operation and dynamic control of FLWLs: 1) significantly increases the power generation and improves the utilization rate of floodwaters compared to those of the original design model; 2) power production rate increases with the acceptable risk but the increasing rate between them decreases with higher levels of risk; 3) results in lower utilization rate of floodwaters compared to that of the deterministic model, but the difference is not significant. The proposed method provides a framework for risk-based decision-making in floodwater utilization to enhance the comprehensive benefits of a multi-reservoir system.

Optimal strategy selection framework for minimizing the economic impacts of severe weather induced power outages

Every year power outages cost billions of dollars and affect millions of people. In the U.S., historical data show that between 2000 and 2016, 75% of the outages (in terms of duration) were caused due to severe weather events. Due to climate change and aging of infrastructures, these weather-related events are getting more frequent. To mitigate the risk of economic losses from severe weather induced power outages, utilities need to invest heavily for building resilience against these events. This research has established a framework for risk-based decision making for identifying optimal risk reduction strategies to minimize the economic impact of severe weather induced power outages. The framework determines the economic impacts of severe weather induced power outages in terms of loss of Gross Domestic Product (GDP) for a state and utilizes a Benefit-Cost Analysis (BCA) based optimization technique to identify the optimal scope of different strategies which will maximize the benefits from them. The benefit has been defined as the reduction of expected GDP loss due to implementation of risk reduction strategies. The framework requires the users to identify strategies, their feasible scope, and cost. This has been illustrated through an example. The proposed framework can be effectively used by the investor-owned utilities for getting their resilience building projects approved from state utility regulatory commissions.

Application of a decision support tool for the risk management of a metro system

ABSTRACT Risk Management assumed over years a central role of safe management of metro railway operation. Since exposure of passengers to hazards cannot be avoided, risk cannot be cancelled but controlled and brought back to acceptable levels. The quantification of system risk level and of the effects of any decision on infrastructural or operational changes must be carefully evaluated. A methodology for in-depth risk analysis within a risk-based decision management is crucial not only to identify the risk, but especially to assess and manage it. This work introduces a complete Risk Management methodology which quantitatively analyses the causes and consequences, assessing all the influence factors of metro safety. Using as example the derailment risk level, an application to an operating metro is presented assessing the effects in terms of safety risk of three different management decisions and highlighting the impact that those choices have on safety.

Failure Interval Probabilistic Analysis for Risk-based Decisions - Concorde Crash Example

The DC-6, DC-8, DC-10, Concorde, Boeing 787 and Boeing 737 MAX fatal crashes and nearmisses were analyzed with event interval probabilistic analysis methods. Fleet grounding decisions are the epitome of risk-based decisions, and the most important decision is the first opportunity to ground. The “first opportunity to ground” decision is retrospectively judged to be wrong if, in the immediate future, another accident or cause-and-effect findings leads to the original decision being reversed. Using only data available at the time of the significant events, the analysis examines these risk-based decisions as if it they were made at the event’s instant in time.&#x0D; The event interval method identified five out of six “first opportunity to ground” decisions correctly, including the Concorde. According to these analyses, the FAA and its predecessor organizations made one correct decision out of five. Use of this method based on statistics and probability would have avoided 503 actual fatalities, plus 9.45 expected value fatalities from additional risk exposure due to flying statistically proven unreliable aircraft. In addition to the reversed decision standard for judging whether these decisions were wrong, the data show that a grounding of the DC-8 and a second grounding of the DC-6 would have been statistically appropriate — but these groundings did not occur.&#x0D; A specific objective of this paper is to lead the FAA and aircraft manufacturers to using event interval probabilistic analysis in grounding decisions and air-worthiness certification. The cause-and-effect data necessary to identify issues and make corrections are often sparse or nonexistent at the time of the event. Cause-and-effect data can take days or months to acquire and analyze, but event interval timing data are simple because system performance data are available at the instant the event occurs.

Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines

The dispersion of inhalable particulates (PM10) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of PM10 in coal mines using AERMOD and assess outcomes rendered by disparate digital elevation models (DEM). CartoDEM (10 m) generated using the rational polynomial coefficient method and publically available DEMs, i.e., SRTM (90 m), ASTER (30 m), CartoDEM (30 m), and FLAT, were processed for simulating complex terrain of coal mines. Modeled concentration predicted using different terrain inputs was compared with field measured values for evaluating performance metrics. This comparison suggested that SRTM and FLAT topography met lesser performance criteria in comparison with other input DEMs. The model performance was evaluated using Willmott’s index of agreement (dr) being 0.39, 0.41, and 0.47 for SRTM, ASTER, and CartoDEM, respectively. However, CartoDEM (10 m) showed a slight improvement with dr of 0.57. The results revealed that model performance improved due to the recentness of DEM rather than its resolution. Overburden dump, haulage routes, and railway siding shared the majority PM10 concentration load invariably in all model runs where peak concentration varied from 454 to 680 µg/m3. Categorically, complex terrain simulations of coal mines influenced dispersion models by altering emission sources’ interaction with pre-processor calculations of meteorological data. The work will help improve the performance of models in complex terrain and the selection of topographic parameterization for risk-based decisions.