Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model

Multisource information fusion for safety risk assessment in complex projects considering dependence and uncertainty

The success of tunneling projects is crucial for infrastructure development. However, the potential leakage risk is particularly challenging due to the inherent uncertainties and fuzziness involved. To address this demanding challenge, a hybrid approach integrating the copula theory, cloud model, and risk matrix, is proposed. The dependence of multiple risk-related influential factors is explored by the construct of the copula-cloud model, and the diverse information is fused by applying the risk matrix to gain a crisp risk result. A case study is performed to test the applicability of the proposed approach, in which a risk index system consisting of nine critical factors is developed and Sobol-enabled global sensitivity analysis (GSA) is incorporated to investigate the contributions of different factors to the risk magnitude. Key findings are as follows: (1) Risk statuses of the studied three tunnel sections are perceived as under grade I (safe), II (low-risk), and III (medium-risk), respectively, and the waterproof material aspect is found prone to deteriorating the tunnel sections. Furthermore, the proposed approach allows for a better understanding of the trends in the risk statuses of the tunnel sections. (2) Strong interactions between influential factors exist and exert impacts on the final risk results, proving the necessity of studying the factor dependence. (3) The developed neutral risk matrix presents a strong robustness and displays a higher recognition capacity in risk assessment. The novelty of this research lies in the consideration of the dependence and uncertainty in multisource information fusion with a hybrid copula-cloud model, enabling to perform a robust risk assessment under different risk matrices with varying degrees of risk tolerance.

Natural background levels, source apportionment and health risks of potentially toxic elements in groundwater of highly urbanized area

Identifying the natural background levels (NBLs), threshold values (TVs), sources and health risks of potentially toxic elements in groundwater is crucial for ensuring the water security of residents in highly urbanized areas. In this study, 96 groundwater samples were collected in urban area of Sichuan Basin, SW China. The concentrations of potentially toxic elements (Li, Fe, Cu, Zn, Al, Pb, B, Ba and Ni) were analyzed for investigating the NBLs, TVs, sources and health risks. The potentially toxic elements followed the concentration order of Fe > Ba > B > Al > Zn > Li > Cu > Ni > Pb. The NBLs and TVs indicated the contamination of potentially toxic elements mainly occurred in the northern and central parts of the study area. The Positive Matrix Factorization (PMF) model identified elevated concentrations of Fe, Al, Li, and B were found to determine groundwater quality. The primary sources of Fe, Al, Pb, and Ni were attributed to the dissolution of oxidation products, with Fe additionally affected by anthropogenic reduction environments. Li and B were determined to be originated from the weathering of tourmaline. High levels of Ni and Cu concentrations were derived from electronic waste leakage, while excessive Ba and Zn were linked to factory emissions and tire wear. The reasonable maximum exposure (RME) of hazard index (HI) was higher than safety standard and reveal the potential health risks in the southwestern study area. Sensitivity analysis demonstrated the Li concentrations possessed the highest weight contributing to health risk. This study provides a valuable information for source-specific risk assessments of potentially toxic elements in groundwater associated with urban areas.

Employing sensitivity analysis to catchments having scanty data

Sensitivity analysis (SA) is generally desirable for parameter optimization, mapping, and calibration in hydrological models, yet the implementation of SA in data-sparse regions is usually avoided due to a lack of continuous data. The present study proposes the novel concept of "minimum continuous data period" to overcome this constraint. It analyses the sensitivity profile of two data-suffice sub-catchments of a data-sparse watershed using data at various timescales to determine the minimum data period required for the SA. The results suggest that the SA employing a minimum data period (2 years in this study) replicated the actual sensitivity profile by an average of 77.5% while replicating the most sensitive and insensitive parameters by 100%. The study encourages the use of the data from sub-catchments to determine the sensitivity profile of the data-sparse catchment. It would benefit in improving the use of SA for rainfall-runoff modelling in data-scanty regions.

About 3D Incompressible Flow Reconstruction from 2D Flow Field Measurements

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily.

Evolution of Temperature Field around Underground Power Cable for Static and Cyclic Heating

Power transmission covering long-distances has shifted from overhead high voltage cables to underground power cable systems due to numerous failures under severe weather conditions and electromagnetic pollution. The underground power cable systems are limited by the melting point of the insulator around the conductor, which depends on the surrounding soils’ heat transfer capacity or the thermal conductivity. In the past, numerical and theoretical studies have been conducted based on the mechanistic heat and mass transfer model. However, limited experimental evidence has been provided. Therefore, in this study, we performed a series of experiments for static and cyclic thermal loads with a cylindrical heater embedded in the sand. The results suggest thermal charging of the surrounding dry sand and natural convection within the wet sand. A comparison of heat transfer for dry, unsaturated and fully saturated sand is presented with graphs and colour maps which provide valuable information and insight of heat and mass transfer around an underground power cable. Furthermore, the measurements of thermal conductivity against density, moisture and temperature are presented showing positive nonlinear dependence.

Investigating the potential of Morris algorithm for improving the computational constraints of global sensitivity analysis

Sensitivity analysis (SA) is widely acknowledged as advantageous and worthwhile in recognizing parameters for model calibration and optimization, especially in complex hydrological models. Although Sobol global SA is an efficient way to evaluate the sensitivity indices, the computational cost is a constraint. This study analyzes the potential of Morris global SA to achieve results tantamount to Sobol SA, at a much cheaper computational expense, using a new approach of increasing the number of replications for the Morris algorithm. SA for two catchments is performed on a coupled hydrological model using Morris and Sobol algorithms. Two target functions are used for each of the algorithms. Sobol SA required 660000 model simulations accounting for about 400 computing hours, whereas increasing the replications from 1000 to 3000, the Morris method called for 63000 runs and 06 computing hours to produce significantly similar results. The Morris parameter ranking improved 50% with respect to Sobol SA by a three-fold increase in replications with a small 5-h increase in the computational expense. The results also suggest that target functions and catchments influence parameter sensitivity. The new approach to employ the Morris method of SA shows promising results for highly parameterized hydrological models without compromising the quality of SA, specifically if there are time constraints. The study encourages the use of SA, which is mainly skipped because of higher computational demands.

Chromium contamination in groundwater and Sobol sensitivity model based human health risk evaluation from leather tanning industrial region of South India

The present investigation was conducted to find the possible chromium contamination in groundwater and the related health risks in a leather industrial region of south India using Sobol sensitivity modeling. Thirty-five groundwater samples were sampled from the field sites and were analyzed for pH, TDS (Total Dissolved Solids), EC (Electrical Conductivity), F^- (Fluoride), NO₃^- (Nitrate) and Cr (Chromium). The concentration of nitrate varied from 3 to 81 mg/L with a mean of 48.6 mg/L. About 57% (n = 20) of the wells surpassed the drinkable limit (45 mg/L) for NO₃^- as per World Health Organization (WHO). The fluoride ion ranged from 0.1 to 2.7 mg/L with a mean of 1.5 mg/L. Around 51% (n = 18) of the samples crossed the recommended limit of WHO for F^- (1.5 mg/L). The chromium varied from 0.01 to 0.19 mg/L in groundwater with a mean of 0.1 mg/L. About 66% (n = 23) of the samples overshoot the permissible limit of WHO standards (0.05 mg/L) for Cr. The spatial distribution map of chromium in the groundwater showed that 271.76 km² area is under risk. Based on total hazard index (THI), 66%, 46%, and 43% of the groundwater samples surpassed the allowable limit (THI > 1) for children, women and men, correspondingly. Children pose severe health risks than women and men in this region. Using Sobol sensitivity indices, three different categories of risk effects were assessed: first order effect (FOE), total effect (TE) and second order effect (SOE). In the oral sensitivity model, concentration of Cr (C_w) in water and ingestion rate (IR) had the dominant role, whereas in the dermal model, skin surface area (SA) and contact fraction by skin (F) had vital role in addition to the concentration (C_w). Further, the outcome of this study insists the responsibilities of industrial, municipal and agricultural sectors to keep the environment pollution free and to ensure the supply of potable water to the people.

Sobol sensitivity analysis for risk assessment of uranium in groundwater

The exposure to uranium (U) in the natural environment is primarily through ingestion (eating contaminated food and drinking water) and dermal (skin contact with U powders/wastes) pathways. This study focuses on the dose assessment for different age-groups using the USEPA model. A total of 156 drinking water samples were tested to know U level in the groundwater of the study region. Different age-groups were selected to determine the human health impact due to uranium exposure in the residing populations. To determine the relative importance of each input, a variance decomposition technique, i.e., Sobol sensitivity analysis, was used. Furthermore, different sample sizes were tested to obtain the optimal Sobol sensitivity indices. Three types of effects were evaluated: first-order effect (FOE), second-order effect (SOE) and total effect. The result of analysis revealed that 17% of the samples had U concentration above 30 µg l^-1 of U, which is the recommended level by World Health Organization. The mean hazard index (HI) value for younger age-group was found to be less than 1, whereas the 95th percentile value of HI value exceeded for both age-groups. The mean annual effective dose of U for adults was found to be slightly higher than the recommended level of 0.1 m Sv year^-1. This result signified that adults experienced relatively higher exposure dose than the children in this region. Sobol sensitivity analysis of FOE showed that the concentration of uranium (C_w) is the most sensitive input followed by intake rate (IR) and exposure frequency. Moreover, the value of SOE revealed that interaction effect of C_w- IR is the most sensitive input parameter for the assessment of oral health risk. On the other hand, dermal model showed C_w- F as the most sensitive interaction input. The larger value of SOE was also recorded for older age-group than for the younger group.

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions.