Numerical analysis of surface hydrogeological water budget to estimate unconfined aquifers recharge

Under changing climate, groundwater resources are the main drivers of socioeconomic development and ecosystem sustainability. This study assessed the contribution of two adjacent watersheds, Muse Street (MS) and West Wood (WW), with low and high urban development, to the Memphis aquifer recharge process in central Jackson, Tennessee, USA. The numerical MODFLOW model was created using data from 2017 to 2019 and calibrated using reported water budget components derived from in-situ data. The calibrated MODFLOW model was then used to investigate the impact of high and low urban developments on the recharge rate. The hydraulic parameters and recharge rates were optimized by adjusting the groundwater level based on the observed water level using PEST. The stochastic modeling was also carried out using the Latin Hypercube approach to reduce the uncertainty. The calibration results were satisfactory, with RMSE of 0.124 and 0.63 obtained in the WW and MS watersheds, respectively, indicating accurate estimation of the input parameters, precisely the hydrodynamic coefficients. The study results indicate that, per unit area, the MS watershed contributes 119% more to recharge and 186% more to riverbed leakage compared to the WW watershed. However, regarding total recharge and riverbed leakage, the WW watershed contributed more than the MS watershed. The results of this study have enhanced the knowledge of the impact of urbanization on hydrology and the recharge process in watersheds with diverse land uses.

An operational approach for large-scale mapping of water clarity levels in inland lakes using landsat images based on optical classification

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm

The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation-optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.

Production of volatile fatty acids by anaerobic digestion of biowastes: Techno-economic and life cycle assessments

Production of volatile fatty acids from food waste and lignocellulosic materials has potential to avoid emissions from their production from petrochemicals and provide valuable feedstocks. Techno-economic and life cycle assessments of using food waste and grass to produce volatile fatty acids through anaerobic digestion have been conducted. Uncertainty and sensitivity analysis for both assessments were done to enable a robust forecast of key-aspects of the technology deployment at industrial scale. Results show low environmental impact of volatile fatty acid with food wastes being the most beneficial feedstock with global warming potential varying from -0.21 to 0.01 CO2 eq./kg of product. Food wastes had the greatest economic benefit with a breakeven selling price of 1.11-1.94 GBP/kg (1.22-2.33 USD) of volatile fatty acids in the product solution determined through sensitivity analysis. Anaerobic digestion of wastes is therefore a promising alternative to traditional volatile fatty acid production routes, providing economic and environmental benefits.

A comparative environmental analysis of conventional and organic rice farming in Thailand in a life cycle perspective using a stochastic modeling approach

System stochasticity is an inherent characteristic of agricultural systems. Many studies have been conducted in Thailand to analyze the rice production systems. However, most of the prior work just focused on deterministic approach to investigate the rice production systems while disregarding the system variability. In this study, the conventional and organic rice farming systems in Thailand were compared considering the uncertainties associated with parameters. The system variability was taken into account by employing a stochastic modeling approach. The considered impact categories include global warming, ozone formation (human health), freshwater ecotoxicity, terrestrial acidification, fine particulate matter formation, freshwater eutrophication, and fossil resource scarcity. The results showed that yield had considerable influence on the environmental profiles of the two systems; organic and conventional farming showed similar results in terms of global warming on a per hectare basis, but the considerable difference was observed on a per tonne basis. The field emissions due to farm inputs were the most significant contributor to most of the impact categories. The fuel used for irrigation, land preparation, and harvesting also contributed significantly to several impact categories. On the other hand, the impacts of inputs production and material transportation were modest. Uncertainty analysis outcomes indicated that there was a noticeable deviation from the deterministic results in terms of global warming and freshwater ecotoxicity. However, when considering the associated uncertainties, no significant difference was observed between the environmental profiles of the two systems.

Integrated evaluation of the performance of phosphogypsum recycling technologies in China

The Chinese government is implementing policies, such as the "Guidance on comprehensive utilization of bulk solid waste for the 14th Five-Year Plan period", to stimulate phosphogypsum (PG) reduction and recycling. Thus, the comprehensive evaluation of PG recycling technologies for sustainable development is crucial. This study proposes a novel multi-criteria decision analysis (MCDA) method that considers the criteria of resources, environment, economy, and society and risk attitudes of decision-makers and integrates game theory (GT) and utility theory for criteria weighting and ranking to assess industrial-scale PG recycling technologies in China. The results demonstrate that GT provides more reasonable criteria weights than individual weighting methods. PG-based lightweight plaster is the top performer in the resource and environmental dimensions owing to its exceptional resource and energy efficiency. PG utilized for dry-mix mortar and organic fertilizer production exhibited the best utility performance of 0.74 and 0.73, respectively. Measures, such as subsidies and product publicity, should be implemented to promote these technologies. However, technologies with poor performance, such as PG used for the co-production of sulfuric acid and fertilizer or cement, may require optimization or substitution for the sustainable recycling of PG. The proposed MCDA method is robust and can serve as a reliable decision-making tool for other waste-recycling technologies. However, caution must be exercised when determining risk attitude using the MCDA method as it may vary with the number of technologies and affect the final rankings.

Simple solutions for improving thermal comfort in huts in the highlands of Peru

In the Peruvian mountains, hundreds of thousands of rural households living in poverty live in cold indoor environments, close to 0 °C. Indoor cold causes thousands of respiratory diseases and excess of winter deaths. In this study, we numerically calculated the impact of simple low-cost refurbishments on discomfort time during a year. Using EnergyPlus and Python, we modelled a typical one-room hut used as bedroom built with a metal-sheet roof, adobe walls, dirt floors, and high infiltration rates. Then, 9 individual solutions were studied, and their combination resulted in 215 different hut designs. The model was calibrated with field measurements to estimate the infiltration. All the numerical calculations included an uncertainty analysis based on Monte Carlo method, and a sensitivity analysis to assess the impact of reducing infiltration on discomfort time. The base case had a discomfort time of 44% of time. The calibration of infiltration resulted in a mean hourly air exchange rate equal to 29.1 h-1 (SD = 17.0 h-1). Five different designs formed the Pareto front that optimized discomfort time and costs. The solution with the lowest discomfort time during a year, 37% of the time, was adding insulation to the roof (U = 0.83 W/m2•K) and the door (U = 1.00 W/m2•K); and its cost was 286USD. In this solution, when infiltrations were reduced to 4.1 h-1 (SD = 4.1 h-1) discomfort time decreased until 16%. These results benefit those households that nowadays invest their limited resources to improve their living conditions but without technical guidance.

Toward understanding the interaction of shale gas-water-carbon nexus in Sichuan-Chongqing region based on county-level water security evaluation

This study develops a comprehensive framework for understanding the interaction of shale gas-water-carbon nexus in Sichuan-Chongqing region. Within this framework, a county-level water security index (WSI) evaluation system is structured. Spatial autocorrelation model and spatial matching degree model are integrated to illustrate the spatial agglomeration characteristics of water security and the water-carbon relationship, respectively. The impacts of shale gas development on water security and carbon emissions are evaluated based on identification of shale well productivity. Results show that about 25.17% of counties with WSI < 0.4 (unsafe), especially in the eastern region. The central cities (such as Chengdu and Neijiang) should take active steps to reach a safety threshold (WSI ≥ 0.6). Population growth can accelerate water security deterioration through uncertainty analysis. Moreover, the spatial matching degree between WSI and carbon emissions in most cities is extremely poor (< 0.5), implying that these cities should optimize their energy structure and promote green transformation. Water used for shale gas extraction can hardly be ignored from a county-scale perspective, especially in Tongliang, Tongnan, and Jianyang. The future shale gas development would pose a threat to the regional climate.

Scale-invariant sensitivity for multi-purpose water reservoirs management with temporal scale-dependent modeling

High-resolution temporal data (e.g., daily) is valuable for the decision-making of water resources management because it more accurately captures fine-scale processes and extremes than the coarse temporal data (e.g., weekly or monthly). However, many studies rarely consider this superior suitability for water resource modeling and management; instead, they often use whichever data is more readily available. So far, no comparative investigations have been conducted to determine if access to different time-scale data would change decision-maker perceptions or the rationality of decision making. This study proposes a framework for assessing the impact of different temporal scales on water resource management and the performance objective's sensitivity to uncertainties. We built the multi-objective operation models and operating rules of a water reservoir system based on daily, weekly, and monthly scales, respectively, using an evolution multi-objective direct policy search. The temporal scales of the input variables (i.e., streamflow) affect both the model structures and the output variables. In exploring these effects, we reevaluated the temporal scale-dependent operating rules under uncertain streamflow sets generated from synthetic hydrology. Finally, we obtained the output variable's sensitivities to the uncertain factors at different temporal scales using the distribution-based sensitivity analysis method. Our results show that water management based on too coarse resolution might give decision makers the wrong perception because the effect of actual extreme streamflow process on the performance objectives is ignored. The streamflow uncertainty is more influential than the uncertainty associated with operating rules. However, the sensitivities are characterized by temporal scale invariance, as the differences of the sensitivity between different temporal scales are not obvious over the uncertainties in streamflow and thresholds. These results show that water management should consider the resolution-dependent effect of temporal scales for balancing modeling complexity and computational cost.

Occurrence and concentrations of organic micropollutants (OMPs) in highway stormwater: a comparative field study in Sweden

This study details the occurrence and concentrations of organic micropollutants (OMPs) in stormwater collected from a highway bridge catchment in Sweden. The prioritized OMPs were bisphenol-A (BPA), eight alkylphenols, sixteen polycyclic aromatic hydrocarbons (PAHs), and four fractions of petroleum hydrocarbons (PHCs), along with other global parameters, namely, total organic carbon (TOC), total suspended solids (TSS), turbidity, and conductivity (EC). A Monte Carlo (MC) simulation was applied to estimate the event mean concentrations (EMC) of OMPs based on intra-event subsamples during eight rain events, and analyze the associated uncertainties. Assessing the occurrence of all OMPs in the catchment and comparing the EMC values with corresponding environmental quality standards (EQSs) revealed that BPA, octylphenol (OP), nonylphenol (NP), five carcinogenic and four non-carcinogenic PAHs, and C16-C40 fractions of PHCs can be problematic for freshwater. On the other hand, alkylphenol ethoxylates (OPnEO and NPnEO), six low molecule weight PAHs, and lighter fractions of PHCs (C10-C16) do not occur at levels that are expected to pose an environmental risk. Our data analysis revealed that turbidity has a strong correlation with PAHs, PHCs, and TSS; and TOC and EC highly associated with BPA concentrations. Furthermore, the EMC error analysis showed that high uncertainty in OMP data can influence the final interpretation of EMC values. As such, some of the challenges that were experienced in the presented research yielded suggestions for future monitoring programs to obtain more reliable data acquisition and analysis.

Long-lead streamflow forecasting using computational intelligence methods while considering uncertainty issue

While some robust artificial intelligence (AI) techniques such as Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) have been frequently employed in the field of water resources, documents aimed to explore their uncertainty levels are few and far between. Meanwhile, uncertainty determination of these AI models in practical applications is highly important especially when we aimed to use the AI models for streamflow forecast due to the repercussions of poorly managed water resources. With the aid of a global daily streamflow dataset, understanding the uncertainty of GEP, MT, and MARS for forecasting streamflow of natural rivers was studied. The efficiency of uncertainty analysis was quantified by two statistical indicators: 95% Percent Prediction Uncertainty (95%PPU) and R-factor. The results demonstrated that MT had lower uncertainty (95%PPU=0.59 and R-factor=1.67) in comparison with MARS (95%PPU=0.61 and R-factor=1.92) and GEP (95%PPU=0.64 and R-factor=2.03). Overall, although the confidence interval bands of uncertainty for the AI models almost captured the mean streamflow measurements, wide bands of uncertainty were obtained and consequently remarkable uncertainty in the calculation of monthly streamflow values was met.

Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data

Groundwater pollution identification is an inverse problem. When solving the inverse problem using regular methods such as simulation-optimization or stochastic statistical approaches, requires repeatedly calling the simulation model for forward calculations, which is a time-consuming process. Currently, the problem is often solved by building a surrogate model for the simulation model. However, the surrogate model is only an intermediate step in regular methods, such as the simulation-optimization method that also require the creation and solution of an optimization model with the minimum objective function, which adds complexity and time to the inversion task and presents an obstacle to achieving fast inversion. In the present study, the extreme gradient boosting (XGBoost) method and the back propagation neural network (BPNN) method were used to directly establish the mapping relationships between the output and input of the simulation model, which could directly obtain the inversion results of the variables to be identified (pollution sources release histories and hydraulic conductivities) based on actual observational data for fast inversion. In addition, to consider the uncertainty of observation data noise, the inversion accuracy of the two machine learning methods was compared, and the method with higher precision was selected for the uncertainty analysis. The results indicated that both the BPNN and XGBoost methods could perform inversion tasks well, with a mean absolute percentage error (MAPE) of 4.15% and 1.39%, respectively. Using the BPNN, with better accuracy for uncertainty analysis, when the maximum probabilistic density value was selected as the inversion result, the MAPE was 2.13%. We obtained the inversion results under different confidence levels and decision makers of groundwater pollution prevention and control can choose different inversion results according to their needs.

The effects of leaflet material properties on the simulated function of regurgitant mitral valves

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated (1) the influence of tissue extensibility and (2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known.

Phylourny: efficiently calculating elimination tournament win probabilities via phylogenetic methods

The prediction of knockout tournaments represents an area of large public interest and active academic as well as industrial research. Here, we show how one can leverage the computational analogies between calculating the phylogenetic likelihood score used in the area of molecular evolution to efficiently calculate, instead of approximate via simulations, the exact per-team tournament win probabilities, given a pairwise win probability matrix between all teams. We implement and make available our method as open-source code and show that it is two orders of magnitude faster than simulations and two or more orders of magnitude faster than calculating the exact per-team win probabilities naïvely, without taking into account the substantial computational savings induced by the tournament tree structure. Furthermore, we showcase novel prediction approaches that now become feasible due to this order of magnitude improvement in calculating tournament win probabilities. We demonstrate how to quantify prediction uncertainty by calculating 100,000 distinct tournament win probabilities for a tournament with 16 teams under slight variations of a reasonable pairwise win probability matrix within one minute on a standard laptop. We also conduct an analogous analysis for a tournament with 64 teams.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11222-023-10246-y.

A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km²) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions.

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Modeling Opioid Abuse: A Case Study of the Opioid Crisis in New England

For the past two decades, the USA has been embroiled in a growing prescription drug epidemic. The ripples of this epidemic have been especially apparent in the state of Maine, which has fought hard to mitigate the damage caused by addiction to pharmaceutical and illicit opioids. In this study, we construct a mathematical model of the opioid epidemic incorporating novel features important to better understanding opioid abuse dynamics. These features include demographic differences in population susceptibility, general transmission expressions, and combined consideration of pharmaceutical opioid and heroin abuse. We demonstrate the usefulness of this model by calibrating it with data for the state of Maine. Model calibration is accompanied by sensitivity and uncertainty analysis to quantify potential error in parameter estimates and forecasts. The model is analyzed to determine the mechanisms most influential to the number of opioid abusers and to find effective ways of controlling opioid abuse prevalence. We found that the mechanisms most influential to the overall number of abusers in Maine are those involved in illicit pharmaceutical opioid abuse transmission. Consequently, preventative strategies that controlled for illicit transmission were more effective over alternative approaches, such as treatment. These results are presented with the hope of helping to inform public policy as to the most effective means of intervention.

Safe CO2 threshold limits for indoor long-range airborne transmission control of COVID-19

CO₂-based infection risk monitoring is highly recommended during the current COVID-19 pandemic. However, the CO₂ monitoring thresholds proposed in the literature are mainly for spaces with fixed occupants. Determining CO₂ threshold is challenging in spaces with changing occupancy due to the co-existence of quanta and CO₂ remaining from previous occupants. Here, we propose a new calculation framework for deriving safe excess CO₂ thresholds (above outdoor level), C _t, for various spaces with fixed/changing occupancy and analyze the uncertainty involved. We categorized common indoor spaces into three scenarios based on their occupancy conditions, e.g., fixed or varying infection ratios (infectors/occupants). We proved that the rebreathed fraction-based model can be applied directly for deriving C _t in the case of a fixed infection ratio (Scenario 1 and Scenario 2). In the case of varying infection ratios (Scenario 3), C _t derivation must follow the general calculation framework due to the existence of initial quanta/excess CO₂. Otherwise, C _t can be significantly biased (e.g., 260 ppm) when the infection ratio varies greatly. C _t can vary significantly based on specific space factors such as occupant number, physical activity, and community prevalence, e.g., 7 ppm for gym and 890 ppm for lecture hall, indicating C _t must be determined on a case-by-case basis. An uncertainty of up to 6 orders of magnitude for C _t was found for all cases due to uncertainty in emissions of quanta and CO₂, thus emphasizing the role of accurate emissions data in determining C _t.

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that E_NS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.

COVID-19 public transit precautions: Trade-offs between risk reduction and costs

Public transit has received scrutiny as a vector for spreading COVID-19 with much of the literature finding correlations between transit ridership and COVID-19 rates by assessing the role that transportation plays as a vector for human mobility in COVID-19 spread. However, most studies do not directly measure the risk of contracting COVID-19 inside the public transit vehicle. We fill a gap in the literature by comparing the risk and social costs across several modes of transportation. We develop a framework to estimate the spread of COVID-19 on transit using the bus system in Pittsburgh. We find that some trips have demand that exceed their COVID-19 passenger limit, where the driver must decide between: (1) leaving a passenger without a ride or (2) allowing them on the bus and increasing COVID-19 risk. We consider five alternatives for alleviating overcapacity: allow crowding, additional buses, longer buses as substitutes, Transportation Network Company (TNC) rides, or Autonomous Vehicles (AVs) for passed-by passengers. We use transit ridership and COVID-19 data from the spring of 2020 by combining transportation data and an epidemiological model of COVID-19 stochastically in a Monte Carlo Analysis. Our results show that 4% of county cases were contracted on the bus or from a bus rider, and a disproportionate amount (52%) were from overcapacity trips. The risk of contracting COVID-19 on the bus was low but worth mitigating. A cost-benefit analysis reveals that dispatching AVs or longer buses yield the lowest societal costs of $45 and $46 million, respectively compared to allowing crowding ($59 million).

Stochastic simulation of seawater intrusion in the Longkou area of China based on the Monte Carlo method

Seawater intrusion is a common groundwater pollution problem, which has a great impact on ecological environment and economic development. In this paper, a numerical simulation model of variable density groundwater was constructed to simulate and predict the future seawater intrusion in Longkou city, Shandong Province of China. The influence of the sensitive parameter uncertainty of the model on the simulation results was evaluated by using the Monte Carlo method. In order to reduce the computational load from repeatedly calling the simulation model, the surrogate model was established by using the support vector regression (SVR) method. After training, the correlation coefficient R² of the input-output relationship between the SVR surrogate model and the seawater intrusion simulation model reached 0.9957, with an average relative error of 0.2%, indicating that the surrogate model has a high fitting accuracy. Stochastic simulations of seawater intrusion showed that the seawater intrusion in the Longkou area will gradually aggravate at a slow rate, and the increase of seawater intrusion in the study area after 30 years was expected to range from - 6.03% to 7.37% at the 80% confidence level.

Uncertainty analysis in probabilistic design of detention rockfill dams using Monte-Carlo simulation model and probabilistic frequency analysis of stability factors

The detention rockfill dams are of promising importance in flood control projects, due to their minimal technical requirement, low cost, minimal environmental side effects, and self-automotive operation process. However, due to the complexity of Non-Darcian flow interactions with stability and uncertainties of dam, the reliable design is a challenging topic. This study aimed to examine the effects of uncertainties in probabilistic design of these dams. We proposed a reliable design framework for detention rockfill dams with a focus on the importance of stability analysis. The effects of design uncertainty sources on the stability of dam, safety factors of overturning, sliding and bearing, along with the hydraulic performance of the dam were examined. The results of the model revealed that the uncertainties in input parameters can effectively regenerate uncertainties in the hydraulic performance ranges from - 53.54 to + 110.11%. The safety factor against the sliding (SFS) has maximum dependencies with the uncertainties ranging - 32.63 to + 87.81%. The Monte-Carlo Simulation (MCS) and fitting probability distribution functions to the safety factor histograms, and uncertainty quantifications results in 88.3%in increasing the safety factors as a reliable methodology for stability design of detention rockfill dams. Thus, the study calls for reliable, certain, and safe design of flood protection rockfill ponds. The ecological evaluation and applying more advanced uncertainty assessment methods remains a future research direction of the current study. The developed framework can be used to acquire future detention rockfill dam design/modeling requirements for reliability-based design optimization as a simulation-optimization model coupled whit MCS.

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

Uncertainty analysis of vehicle-pedestrian accident reconstruction based on unscented transformation

In order to investigate the sensitivity of parameters and analyze the uncertainty of reconstructed results in traffic accident, the impact of correlations between parameters on accident reconstruction results was taken into account using uncertainty analysis. Based on unscented transformation (UT), a parameter sensitivity analysis method and an efficient uncertainty analysis method in accident reconstruction were proposed. Sensitivity analysis was performed through the sigma point sets generated by the UT method. A first-order response surface model was constructed to analyze the sensitivity of accident reconstruction parameters combined with regression analysis, which is more flexible and controllable than the general experimental design. For the uncertainty analysis of the reconstructed results, the other methods have been used to demonstrate the validity of the proposed method, including the first second-order method of moments (FOSM), the uncertainty theory, and the Monte Carlo (MC) methods, through analyzing the numerical and real-world cases. The results show that the presented method has high accuracy, significantly reduces the computational burden, and does not depend on the distribution type of variables. When considering the effect of the correlation between parameters of the vehicle-pedestrian crash on accident reconstruction results, the results show that the correlation coefficient between random variables had a much more significant impact on the standard deviation of vehicle speed than on the mean value of vehicle speed. Regardless of negative or positive correlations, the relative error of standard deviation of vehicle speed increased continuously as the correlation increased, reaching 52%. The proposed method is effective and reliable for vehicle collision accident reconstruction under uncertainty and correlation, which can provide more comprehensive information in accident reconstruction.