Uncertainty Analysis Research Articles

Quantitative risk analysis and parameter sensitivity evaluation of wellbore instability in poroelastic media considering uncertainty of geomechanical parameters

The randomness and fuzziness of geomechanical parameters make it difficult to directly determine the wellbore collapse and fracture pressures, and thus show significant influence on the risk assessment of drilling operations. Therefore, to effectively analyze the risk of wellbore instability in deep formation, this paper develops the evaluation models of wellbore stability for the vertical well drilled through the poroelastic rock formation subjected to borehole stresses under different time domains, including the instantaneous, modified instantaneous, short-time, long-time, and elastic ones. Meanwhile, based on the Monte Carlo method, one compares the difference between Taylor expansion and the Rosenbluth method in quantifying the uncertainty of the evaluation models. One analyzes the risk of wellbore instability under the condition that the geomechanical parameters satisfy the uniform, triangular, and normal distribution characteristics. In addition, different from the single factor sensitivity evaluation in previous uncertainty analysis, one introduces Morris and Sobol methods to conduct the comprehensive analysis of local and global sensitivity of geomechanical parameters. The main results show that the collapse pressure determined by the different cases of borehole stresses presents in descending order of short-time, long-time, and instantaneous. However, the fracture pressure shows in ascending order of long-time, instantaneous, and short-time cases. Besides, the different distribution characteristics of geomechanical parameters lead to the relationship of safe mud weight window in descending order of normal distribution, triangular, uniform distributions, and the uncertainty characteristics caused by the normal distribution of geomechanical parameters can be effectively quantified by Taylor expansion and Rosenbluth method. The strong sensitivity parameters affecting wellbore collapse and fracture are the maximum and the minimum horizontal in situ stresses, respectively. These two parameters present a strong difference in global sensitivity and local sensitivity analysis, which is manifested by the comprehensive effect of global parameters. The influence of the maximum horizontal in-situ stress on borehole collapse is weakened to a certain extent, while wellbore tensile fracturing is strengthened. On the contrary, the minimum horizontal in-situ stress is characterized by enhancing the borehole collapse whereas weakening the tensile fracturing. Besides, the influence degree increases with the increasing of parameter uncertainty.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

ACE Surrogate Model–Based Uncertainty and Sensitivity Analysis Methods for Severe Accident Codes

This paper explores the alternating conditional expectation (ACE) algorithm-based surrogate model to advance the state-of-practice in uncertainty and sensitivity analysis methodologies for severe accident codes. For engineering purposes, the ACE algorithm has been used as an alternative means to find the optimal functional forms of the multiple input variables and response variables of interest. Analysis results here demonstrate that compared with the reference cases the proposed surrogate model provides much higher performance in terms of the coefficient of determination (R2) and normalized root mean square error (NRMSE), thus giving more robust insights into the relationship and correlation between the input parameters and figures of merit (FOMs) of interest. Relevant results and insights are summarized in terms of points of interest.

Virtual Hydrological Laboratories: Developing the Next Generation of Conceptual Models to Support Decision Making Under Change

AbstractAs hydrological systems are pushed outside the envelope of historical experience, the ability of current hydrological models to serve as a basis for credible prediction and decision making is increasingly challenged. Conceptual models are the most common type of surface water hydrological model used for decision support due to reasonable performance in the absence of change, ease of use and computational speed that facilitate scenario, sensitivity and uncertainty analysis. Hence, conceptual models in effect represent the current “shopfront” of hydrological science as seen by practitioners. However, these models have notable limitations in their ability to resolve internal catchment processes and subsequently capture hydrological change. New thinking is needed to confront the challenges faced by the current generation of conceptual models in dealing with a changing environment. We argue the next generation of conceptual models should combine the parsimony of conceptual models with our best available scientific understanding. We propose a strategy to develop such models using multiple hydrological lines of evidence. This strategy includes using appropriately selected physically resolved models as “Virtual Hydrological Laboratories” to test and refine the simpler models' ability to predict future hydrological changes. This approach moves beyond the sole focus on “predictive skill” measured using metrics of historical performance, facilitating the development of the next generation of conceptual models with hydrological fidelity (i.e., models that “get the right answers for the right reasons”). This quest is more than a scientific curiosity; it is expected by policy makers who need to know what to plan for.

Models, simulations, and applications of small satellite thermal analysis

Progress in satellite technology places higher expectations on the design period and reliability of thermal control systems, especially for small satellites with high intensification. Here, a thermal analysis is conducted throughout the satellite development process, with thermal modeling serving as the foundation for thermal analysis in the early and formal design and test verification stages. As a result, designers are required to appreciate, penetrate, and properly apply thermal models for the development of satellite thermal systems. Thus, in this article, we reviewed the research and development of thermal model techniques, as well as the preparation of crucial parameters and means of simplification for accurate and fast solutions. In addition, a summary of the relevant application and state-of-the-art technologies such as uncertainty and sensitivity analysis, intelligent optimization design, and parameter correlation, was dedicated to promoting the comprehensive development and innovation of thermal analysis of satellites.

Reliability and sensitivity analyses of porous functionally graded graphene platelet reinforced composite plate

The performance of composite is seriously influenced by numerous uncertain factors, in which the uncertainty analysis plays a vital role in composite materials analysis by providing a comprehensive understanding of uncertain factors and their influence on design and performance. In this study, uncertain behaviors of porous functionally graded graphene platelets reinforced composite (FG-GPLRC) plates are first investigated for assessing the safety under bending deformation. The mechanical model for static analysis of the porous FG-GPLRC plate is formulated by Reddy’s higher-order shear deformation theory, and the bending responses are derived by Hamilton’s principle and the Navier method. Material properties and loads are deemed as random variables, and the performance function is determined by the Tsai-Wu criterion. The univariate dimension reduction method and maximum entropy method are employed to estimate the reliability of porous FG-GPLRC plates with diverse distribution patterns. Additionally, the analytical sensitivities of failure probability with respect to material parameters are derived. The analysis results illustrate that the existence of uncertain factors significantly influences the performance and reliability of composites, and the structural reliability can be improved by adding appropriate pores and graphene platelets at the edge layer of the composite plate.

Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting

AbstractKarst springs are essential drinking water resources, however, modeling them poses challenges due to complex subsurface flow processes. Deep learning models can capture complex relationships due to their ability to learn non‐linear patterns. This study evaluates the performance of the Transformer in forecasting spring discharges for up to 4 days. We compare it to the Long Short‐Term Memory (LSTM) Neural Network and a common baseline model on a well‐studied Austrian karst spring (LKAS2) with an extensive hourly database. We evaluated the models for two further karst springs with diverse discharge characteristics for comparing the performances based on four metrics. In the discharge‐based scenario, the Transformer performed significantly better than the LSTM for the spring with the longest response times (9% mean difference across metrics), while it performed poorer for the spring with the shortest response time (4% difference). Moreover, the Transformer better predicted the shape of the discharge during snowmelt. Both models performed well across all lead times and springs with 0.64–0.92 for the Nash–Sutcliffe efficiency and 10.8%–28.7% for the symmetric mean absolute percentage error for the LKAS2 spring. The temporal information, rainfall and electrical conductivity were the controlling input variables for the non‐discharge based scenario. The uncertainty analysis revealed that the prediction intervals are smallest in winter and autumn and highest during snowmelt. Our results thus suggest that the Transformer is a promising model to support the drinking water abstraction management, and can have advantages due to its attention mechanism particularly for longer response times.

Regional carbon emission pathways under central-local coordination: Objective constraints and collaborative incentives

The commitment of carbon peak and carbon neutrality in China has attracted global attention. In high-emission countries, the coordination between the central and local governments will inevitably influence climate governance. This study establishes the Regional Collaborations Dynamic Integrated model of Climate and Economy, predicting carbon emission pathways and social cost of carbon for regions within China. The design of six scenarios incorporates objective constraints and collaborative incentives to identify the impact of central-local coordination on climate governance. Results indicate that objective constraints will mitigate 0.17–0.60 Gt carbon emissions in all regions, reaching carbon peak 5–10 years earlier. Collaborative incentives will contribute to achieving carbon neutrality for regions in the long term, especially the Yangtze River Economic Belt advancing 7 years. The combination of objective constraints and collaborative incentives will facilitate China reaching carbon peak in 2029 and achieving carbon neutrality in 2057. The social cost of carbon will vary across regions due to socioeconomic conditions, initiative choices, and mitigation efforts. Moreover, the analysis of structural uncertainty can help China to undertake timely climate actions and avoid excessive mitigation costs. Based on these findings, this study proposes policy suggestions to improve climate governance by dynamic objective management, innovative collaborative forms, enhancing synergistic effects.

Comparative analysis of dosimetric uncertainty using Gafchromic™ EBT4 and EBT3 films in radiochromic film dosimetry

Advancements in radiotherapy dosimetry aim to enhance precision and accuracy, with Gafchromic™ EBT4 film emerging as a promising tool. However, its performance compared to EBT3 remains underexplored, necessitating a comprehensive analysis of dosimetric uncertainties. This study evaluates the efficacy of EBT4 in radiochromic dosimetry, comparing it to EBT3. Employing a 6 MV beam quality, absorbed doses ranging from 0 to 10 Gy were investigated using different measurement quantities and fitting functions. Gafchromic™ EBT3 and EBT4 films (8′′ × 10″) were irradiated in a water-equivalent phantom, with calibration doses delivered using a Varian Truebeam Stx linear accelerator. Two measurement quantities, pixel values (PV) and net change transmittance (netΔT), were analyzed, along with calibration functions including a rational function for PV and a logarithmic function for netΔT. Uncertainty analyses considered experimental, fitting, and calibration dose uncertainties. Dose-response curves for EBT3 and EBT4 films demonstrated robust fits, emphasizing the reliability of both. EBT4 consistently exhibited lower uncertainties, particularly below 2 Gy, showcasing superior precision in low-dose scenarios. The choice of response quantity and calibration function influenced results, with PV-rational function excelling for doses 2–10 Gy, and netΔT-logarithmic function showing enhanced precision in the 0–2 Gy range. The uncertainty analysis emphasized EBT4's superior performance, especially in low-dose ranges, with lower relative uncertainties and absolute errors, indicating enhanced reliability in dose measurements. The study underscores EBT4's potential to contribute to improved accuracy in various clinical scenarios. However, users should tailor the choice of response quantities and calibration functions to specific dose calibration ranges, suggesting a nuanced approach.

Design of polycrystalline metallic alloys under multi-scale uncertainty by connecting atomistic to meso‑scale properties

Uncertainty quantification plays a pivotal role in advancing the development of reliable and high-performance material designs via multiscale materials modeling. This study focuses on modeling the uncertainty of meso‑scale mechanical properties, e.g. modulus of elasticity and yield strength of Ti-7Al, by incorporating the inherent randomness of substitutional atoms of the alloy and its microstructural texture. A molecular dynamics approach is employed to probe the epistemic uncertainty of single-crystal properties. Moreover, the aleatoric uncertainty is quantified using the experimental microstructural texture data of a previous investigation. The propagation of the epistemic and aleatoric uncertainty on the homogenized properties is investigated using an analytical uncertainty quantification method. Subsequently, the Ti-7Al alloy microstructures are designed to improve meso‑scale mechanical properties under uncertainty by integrating the analytical method into the optimization scheme.

Calibration, validation and uncertainty analysis of a SWAT water quality model

Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers.

Ensemble Learning-Based Pain Intensity Identification Model Using Facial Expressions

The pain identification model (PIM) can assist the healthcare professionals to render effective services to individuals. Disabled individuals can benefit from the automated PIM. Ensemble learning is widely employed for developing medical applications. A model for classifying the pain intensity using facial expression images is proposed in this study. A ShuffleNet V2 model is fine-tuned to extract features using fusion feature and class activation map techniques. CatBoost and XGBoost models are used as base models to predict pain intensities. The authors used the support vector machine (SVM) model as a meta-model to produce a final outcome. They optimize the SVM model in order to identify pain using the predictions of the base models. The model is generalized using the University of Northern British Columbia–McMaster dataset. The dataset encompasses 200 videos and 48,000 annotated images. The comparative analysis outcome highlights the exceptional performance of the proposed PIM. An optimal accuracy of 98.7% and an F1-score of 98.0% indicate the effectiveness of the proposed model. The uncertainty analysis outcome revealed that the model is reliable and can be deployed in healthcare centers. However, substantial training is required to boost the efficiency of the proposed model in real-time settings.

Stochastic optimization of solar-based distributed energy system: An error-based scenario with a day-ahead and real-time dynamic scheduling approach

Distributed energy systems (DES) have garnered global attention as a promising solution for the expansion of renewable energy sources. However, stochastic uncertainty in renewable sources poses significant challenges in the collaboration optimization of system design and operation. In this study, a comparison analysis was conducted to assess the effectiveness of the conventional distribution-based scenario generation (DS) method for stochastic optimization of a distributed energy system in residential buildings. The results revealed that the DS method inaccurately captured extreme scenarios and exhibited limitations in operation optimization, leading to significant performance evaluation bias. To address these challenges, a novel stochastic optimization approach was developed based on error-based scenarios and a day-ahead and real-time dynamic scheduling strategy (ES-DRS). This approach incorporated solar energy prediction errors to more accurately characterize extreme scenarios, while also considering dynamic dual-scale meteorological boundary condition for rolling operation optimization. Furthermore, the buffer storage and coverage periods in ES-DRS were investigated and discussed during dynamic scheduling. Results demonstrated that when the buffer storage and coverage period were set at 36.22 kWh in summer and 24 h, respectively, the DES with ES-DRS achieved optimal multi-objective performance. This resulted in an annual total cost of 3.21 × 104 USD and CO2 emission of 5.82 tons, representing reductions of 26.45% and 61.06%, respectively, compared to the conventional strategy. Overall, this research contributes to advancing uncertainty analysis and scenario-based optimization in DES, highlighting the potential benefits of adopting the ES-DRS approach to maximize overall performance from both economic and environmental perspectives.

Biomonitoring and health risk assessment of exposure to phthalate esters in waste management workers

Humans are at risk of exposure to phthalates due to the widespread use of plasticized plastics, and one of the major concerns is occupational exposure. The present study investigated occupational exposure to phthalates at one of the greatest solid waste management sites in the second-largest country in the Middle East. Carcinogenic and non-carcinogenic health risks were assessed by human biomonitoring (HBM). The concentration of phthalate esters was determined using gas chromatography-mass spectrometry (GC–MS), and the daily intake (DI) of phthalate was calculated based on the adjusted urinary creatinine concentrations. Moreover, carcinogenic and non-carcinogenic risks were assessed. Monte Carlo simulations were performed for uncertainty and sensitivity analysis. The highest concentration recorded was 130.80 µg/g creatinine for mono-ethyl phthalate (MEP) among the composting group, while the lowest concentration was 0.49 µg/g creatinine for Monobenzyl phthalate (MBzP) among the office group. All estimates of daily intake were below the reference concentration, and differences between the DI at site sections were statistically significant (p < 0.05). The non-carcinogenic risk level was negligible. The excess lifetime cancer risk (ELCR) values corresponding to di-(2-ethylhexyl) phthalate (DEHP) exposure were 2.07E-04 among the composting group and 2.07E−04 among the processing group, posing a definite risk. The carcinogenic risk value among the office group was in a possible risk category with ELCR values of 9.75 E−05. The on-site workers of waste management sites can be highly exposed to phthalates, and their health risk is considerable. Appropriate measures and interventions should be considered to reduce occupational exposure to phthalates.

Regional assessment and uncertainty analysis of carbon and nitrogen balances at cropland scale using the ecosystem model LandscapeDNDC

Abstract. The assessment of cropland carbon and nitrogen (C and N) balances plays a key role in identifying cost-effective mitigation measures to combat climate change and reduce environmental pollution. In this paper, a biogeochemical modelling approach is adopted to assess all C and N fluxes in a regional cropland ecosystem of Thessaly, Greece. Additionally, the estimation and quantification of the modelling uncertainty in the regional inventory are realized through the propagation of parameter distributions through the model, leading to result distributions for modelling estimations. The model was applied to a regional dataset of approximately 1000 polygons, deploying model initializations and crop rotations for the five major crop cultivations and for a time span of 8 years. The full statistical analysis on modelling results (including the uncertainty ranges given as ± values) yields for the C balance carbon input fluxes into the soil of 12.4 ± 1.4 t C ha−1 yr−1 and output fluxes of 11.9 ± 1.3 t C ha−1 yr−1, with a resulting average carbon sequestration of 0.5 ± 0.3 t C ha−1 yr−1. The averaged N influx was 212.3 ± 9.1 kg N ha−1 yr−1, while outfluxes of 198.3 ± 11.2 kg N ha−1 yr−1 were estimated on average. The net N accumulation into the soil nitrogen pools was estimated to be 14.0 ± 2.1 kg N ha−1 yr−1. The N outflux consists of gaseous N fluxes composed of N2O emissions of 2.6 ± 0.8 kg N2O–N ha−1 yr−1, NO emissions of 3.2 ± 1.5 kg NO–N ha−1 yr−1, N2 emissions of 15.5 ± 7.0 kg N2–N ha−1 yr−1 and NH3 emissions of 34.0 ± 6.7 kg NH3–N ha−1 yr−1, as well as aquatic N fluxes (only nitrate leaching into surface waters) of 14.1 ± 4.5 kg NO3–N ha−1 yr−1 and N fluxes of N removed from the fields in yields, straw and feed of 128.8 ± 8.5 kg N ha−1 yr−1.

A life cycle assessment of clinker and cement production in Ethiopia

Cement production is a major consumer of energy and the largest source of industrial CO2 emissions. This study aims to perform an environmental life cycle assessment of clinker and cement production in Ethiopia, using ReCiPe impact assessment method. Inventory data (material, energy, and transportation) is collected from seven major Ethiopian cement industries. The midpoint analysis identified nine hotspot environmental concerns: global warming, ozone formation (human health and terrestrial ecosystem), particulate matter formation, terrestrial (acidification and ecotoxicity), freshwater eutrophication, human carcinogenic toxicity, and fossil resource scarcity. Human health emerged as the most significantly affected endpoint damage category by the midpoint impacts. Among the process stages included in clinker system boundary, clinker production phase (kiln emissions) is a significant contributor to the total score of the hotspot impacts, ranging from 60.7% to 91.8%. The clinker system is responsible for over 81.03% of the overall environmental burden of cement. The sensitivity analysis reveals that a 5% change in kiln energy consumption and transportation burden could lead to a reduction in hotspot impacts ranging from 1.8% to 5%. To foster reliability of this study, uncertainty analysis is also conducted. Overall, the findings indicate the need to enhance environmental sustainability in Ethiopian cement production.

Wind turbine condition monitoring based on three fitted performance curves

AbstractBased on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a novel unsupervised SVM‐KNN model, integrated by support vector machine (SVM) and k nearest neighbour (KNN), is the optimum detector for PC refinements. Based on the refined data by the SVM‐KNN detector, several common nonparametric regressors have largely improved their prediction accuracies on pitch angle and rotor speed curves from roughly 86% and 90.6%, respectively, (raw data) to both 99% (refined data). Noticeably, under the SVM‐KNN refinement, the errors have been reduced by roughly five times and 10 times for pitch angle and rotor speed predictions, respectively. Ultimately, bootstrapped prediction interval is applied to conduct the uncertainty analysis of the optimal predictive regression model, reinforcing the performance monitoring and anomaly detection.

An ultra-rapid optical gas standard for dynamic processes: Absolute NH3 quantification and uncertainty evaluation

An ultra-rapid Optical Gas Standard (OGS) for absolute NH3 quantification in the shock tube has been developed. For the first time, the absolute NH3 mole fractions before the incident shock wave, after the incident shock wave and immediately after the reflected shock wave (within 25 μs) were quantified using OGS. On that basis, spectrally resolved NH3 absorption cross-section datasets were measured over pressure ranges of 1.15–3.15 bar and elevated temperatures (up to 2680 K), facilitating NH3 mole fraction quantification after the reflected shock wave using a correlative linear fit method. Besides, comprehensive uncertainty evaluations were conducted for thermodynamic parameters and species mole fraction following the 'Guide to the Expression of Uncertainty in Measurement (GUM)'. This metrological uncertainty analysis not only contributes to providing high-quality data but also serves as a valuable reference for other traceable measurements in shock tubes, such as dynamic temperature and pressure calibrations.

Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization.

Transactive energy management of solar-based range anxious electric vehicle integrated parking lots

Electric vehicles (EVs) are regarded as essential solutions for alleviating climate change and energy crises. EVs can store excess Photovoltaic (PV) generation and transfer energy to other EVs, reducing distribution network upgrade costs. However, the limited range of EVs coupled with inadequate charging infrastructure leads to range anxiety among EV users, thereby becoming a barrier to implementing a transactive energy management system. This research quantifies the range anxiety among EV users and proposes a novel trading mechanism for transactive trading between workplace EVs. The case study solved for a commercial region in Auckland shows a 3–10% reduction in charging cost compared to a conventional V2G system. Further analysis shows that public charging stations can also result in cost savings from 1% to 5%. Still, their impact is limited compared to the number of discharging EVs participating in transactive trading. The uncertainty analysis of PV generation under different scenarios also shows the cost savings of the proposed strategy. The simulation results verify the feasibility and effectiveness of the proposed strategy while alleviating range anxiety among EV users.