Uncertainty Analysis Research Articles

Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis

The spatial mapping and prediction of groundwater quality (GWQ) is important for sustainable groundwater management, but several research gaps remain unexplored, including the inaccuracy of spatial interpolation, limited consideration of the geological environment and human activity effects, limitation to specific pollutants, and unsystematic indicator selection. This study utilized the entropy-weighted water quality index (EWQI), the LightGBM model, the pressure-state-response (PSR) framework and SHapley Additive exPlanations (SHAP) analysis to address the above research gaps. The normalized importance (NI) shows that NO3− (0.208), Mg2+ (0.143), SO42− (0.110), Cr6+ (0.109) and Na+ (0.095) should be prioritized as parameters for remediation, and the skewness EWQI distribution indicates that although most sampled locations have acceptable GWQ, a few areas suffer from severely poor GWQ. The PSR framework identifies 13 indicators from geological environments and human activities for the SMP of GWQ. Despite high AUROCs (0.9074, 0.8981, 0.8885, 0.9043) across four random training and testing sets, it was surprising that significant spatial uncertainty was observed, with Pearson correlation coefficients (PCCs) from 0.5365 to 0.8066. We addressed this issue by using the spatial-grid average probabilities of four maps. Additionally, population and nighttime light are key indicators, while net recharge, land use and cover (LULC), and the degree of urbanization have the lowest importance. SHAP analysis highlights both positive and negative impacts of human activities on GWQ, identifying point-source pollution as the main cause of the poor GWQ in the study area. Due to the limited research on this field, future studies should focus on six key aspects: multi-method GWQ assessment, quantitative relationships between indicators and GWQ, comparisons of various spatial mapping and prediction models, the application of the PSR framework for indicator selection, the development of methods to reduce spatial uncertainty, and the use of explainable machine learning techniques in groundwater management.

Expert-based prior uncertainty analysis of gridded water balance components: Application to the irrigated Hindon River Basin, India

Study regionHindon River Basin, North India. Study focusAccurate estimation of water balance components is crucial for water management applications yet challenging due to errors in monthly gridded water balance data products. Error and uncertainty quantification is especially important in the absence of extensive in-situ data. This paper presents a prior uncertainty analysis for such situations consisting of two components: (i) quantification of prior uncertainties using metrics that quantify errors in individual products and variability and consistency between products, and (ii) reduction of prior uncertainties by eliminating unrealistic water balance estimates. New hydrological insights for the regionGrid-scale inter-product uncertainty or variability, computed as the coefficient of variation (CV, %) at various temporal scales, reveals discrepancies between water balance products due to a combination of factors, including methodological differences, inherent spatial variability, data sources, and resolution disparities. At the mean annual scale, P fluxes display a lower grid-scale inter-product uncertainty (5–9 %) than ET (20–55 %), while the ∆TWS from GRACE solutions show a moderate mean annual grid-scale inter-product uncertainty (15–19 %). Grid-scale inter-product uncertainties of ∆SMS for July – representing the onset of the monsoon season – are high (CV = 54–122 %), indicating that the uncertainty in estimates of this component may have a large impact on water balance analyses. P fluxes exhibited fewer spatio-temporal uncertainties (R2 above 0.8) than ET fluxes (R2 less than 0.75). The exclusion of the unreliable data sets resulted in (a) reducing uncertainties in input water balance components with triple collocation range shifting from 15–38 to 17–23 mm/month for ET and from 16–52 to 11–23 mm/month for P, (b) obtaining updated prior estimates of seasonal water balance. The updated priors of water balance variables per season suggest a net basin outflow (from −318 to −57 mm/season) during the monsoon (rainy) season and net basin inflow (from −38 to 330 mm/season) during the non-monsoon (dry) season, the latter related to surface-water imports from outside the basin. All GRACE data sets exhibit a regional long-term decreasing trend in total water storage (ranging from −31 to −61 mm/year), qualitatively confirming previously documented unsustainable groundwater depletion in the basin. Prior ranges and uncertainties for all water balance variables reported here can be used as input into a posterior analysis that uses in-situ data for locally calibrating (bias-correcting, noise-filtering) and further updating the prior estimates.

NH3/C2H6 and NH3/C2H5OH oxidation in a shock tube: Multi-speciation measurement, uncertainty analysis, and kinetic modeling

This study examines NH3, NO, and CO profiles during NH3/C2H6 and NH3/C2H5OH oxidation in a shock tube using laser absorption spectroscopy at 1317–1957 K and ∼2.8 bar, with 5–20% C2H6 or C2H5OH, equivalence ratios of 0.5/1.0/1.5, and a 0.9 argon dilution ratio. Metrological uncertainty analysis on mole fractions have been performed. A novel dynamic uncertainty methodology is proposed to time-resolved speciation profiles. From the experimental measurements, the promotional effect on ammonia ignition by C2H6 or C2H5OH is identical under investigated conditions. The multi-speciation profiles demonstrate reasonable relations among the consumption of NH3 and the formation of NO and CO, which show strong temperature and equivalence ratio dependence. An updated PTB-NH3/C2 1.1 mechanism accurately predicts ignition delay times and speciation profiles. Sensitivity analysis reveals NH3 chemistry’s significant control, with NH3+O2/H/OH and N2H2+M reactions being critical at >1300 K, and HO2 interactions being pivotal at 800–1200 K. The reactions related to N2H2 and N-C chemistries dominate for fuel-rich mixtures at high temperatures, resulting in a reverse equivalence ratio dependence on IDTs, namely fuel-rich mixtures exhibit the lowest reactivity. The N-C chemistries, particularly the pronounced reaction pathway of HCN→CN→NCO→HNCO→CO play a crucial role in maintaining the CO level constant after reaching its peak position under fuel-rich conditions·NH3 consumption pathways shift from NH2→H2NO→HNO→NO→NO2→N2O→N2 at intermediate temperatures to NH3→NH2→NH→N(HNO)→NO→(N2O)N2 at high temperatures. The C2-additives participate in the oxidation process by introducing additional OH/H/CH3 radicals, thereby facilitating the reactive radical pool and developing the N-C intersystem-crossing pathways. In summary, the extensive multi-speciation data, metrological uncertainty analysis, mechanism validation and development, together with comprehensive kinetic modelling can be valuable resources for further studies of ammonia combustion.

How Model Organisms and Model Uncertainty Impact Our Understanding of the Risk of Sublethal Impacts of Toxicants to Survival and Growth of Ecologically Relevant Species.

Understanding how sublethal impacts of toxicants affect population-relevant outcomes for organisms is challenging. We tested the hypotheses that the well-known sublethal impacts of methylmercury (MeHg) and a polychlorinated biphenyl (PCB126) would have meaningful impacts on cohort growth and survival in yellow perch (Perca flavescens) and Atlantic killifish (Fundulus heteroclitus) populations, that inclusion of model uncertainty is important for understanding the sublethal impacts of toxicants, and that a model organism (zebrafish Danio rerio) is an appropriate substitute for ecologically relevant species (yellow perch, killifish). Our simulations showed that MeHg did not have meaningful impacts on growth or survival in a simulated environment except to increase survival and growth in low mercury exposures in yellow perch and killifish. For PCB126, the high level of exposure resulted in lower survival for killifish only. Uncertainty analyses increased the variability and lowered average survival estimates across all species and toxicants, providing a more conservative estimate of risk. We demonstrate that using a model organism instead of the species of interest does not necessarily give the same results, suggesting that using zebrafish as a surrogate for yellow perch and killifish may not be appropriate for predicting contaminant impacts on larval cohort growth and survival in ecologically relevant species. Our analysis also reinforces the notion that uncertainty analyses are necessary in any modeling assessment of the impacts of toxicants on a population because it provides a more conservative, and arguably realistic, estimate of impact. Environ Toxicol Chem 2024;43:2122-2133. © 2024 SETAC.

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Hybrid photovoltaic-thermoelectric generator (PV-TEG) system combines two types of energy conversion which is an important innovation to advance the development of renewable energy technologies. Hybrid system in practice needs to track the best operating point in real-time with the help of maximum power point tracking (MPPT) technology to ensure the system outputs maximum energy and optimizes performance. To improve the energy conversion efficiency and utilization of hybrid PV-TEG systems, as well as to effectively combat the negative impacts under partial shading conditions (PSC) and non-uniform temperature distribution (NTD), this paper proposes a hybrid system MPPT technique based on the bi-directional long and short-term neural networks (Bi-LSTM). The Bi-LSTM can consider both past and future data information, which will enhance the training process of the network and the accuracy of the model, making the results more comprehensive. It approaches the global maximum power point by searching for the maximum power down in the forward layer and up in the backward layer. The algorithm can effectively prevent from falling into local optimum under PSC and NTD. The case study section tests and explores the feasibility of utilizing Bi-LSTM to obtain the maximum power from a hybrid PV-TEG system under five case study scenarios, namely, start-up test, step change in solar irradiance, stochastic variation, Hong Kong four-season real case, and uncertainty analysis. Comparative analysis with the other five algorithms are also conducted to thoroughly verify the superiority of the proposed technique for hybrid system MPPT applications. The simulation results indicate that the hybrid PV-TEG system with Bi-LSTM achieves the optimal MPPT performance stably and efficiently under different operating conditions. In particular, low irradiate condition in spring, the energy generated by Bi-LSTM exceeds the INC, SAO and P&O energy outputs by 80.51%, 61.22%, and 31.04%, respectively.

Gas–liquid mass-transfer characteristics during dissolution and evolution in quasi-static and dynamic processes

This study aims to understand the comprehensive behavior of the gas–liquid flow and dissolution–evolution mass transfer. A quasi-static closed-tank experiment was designed to measure the static mass-transfer coefficients of the dissolution and evolution processes using the diffusion equation. After a detailed uncertainty analysis, a dynamic ventilated-pipe experiment with different-sized orifice plates was designed to illustrate the relationship between the hydrodynamic parameters, physical structure, and gas–liquid mass-transfer characteristics. The results showed that, as the static pressure and liquid-level height increase, both the dissolution and evolution coefficients exhibit increasing trends. However, when the physical condition reaches the initial state after pressurization and depressurization, the gas absorbed by the solution cannot completely evolve from the solution; that is, the dissolution rate is always greater than or equal to the evolution rate. For the equal-diameter pipe, as the gas flow rate increases, the concentration increment decreases slightly after reaching the peak, owing to the reduction in mass-transfer time caused by the increase in liquid flow rate. In particular, the maximal dissolved concentration, an increment of 210.9 %, occurred in the double large-orifice plate with the ventilated condition, far exceeding the maximum value in the quasi-static process. Moreover, the concentration under the layout of two small-orifice plates decreases slightly, and the larger gas content enables the solution to have more gas nuclei, making it easier to induce the gas evolution. The current study provides guidance for the gas–liquid-mixture transportation and improvement of the dissolved efficiency.

Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review

Artificial Intelligence (AI) methods, including Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs), Support Vector Machines (SVMs), Deep Learning (DL), Genetic Programming (GP) and Hybrid Algorithms, have proven to be important tools for accurate groundwater level (GWL) modelling. Through an analysis of the results obtained in numerous articles published in high-impact journals during 2001–2023, this comprehensive review examines each method’s capabilities, their combinations, and critical considerations about selecting appropriate input parameters, using optimisation algorithms, and considering the natural physical conditions of the territories under investigation to improve the models’ accuracy. For example, ANN takes advantage of its ability to recognise complex patterns and non-linear relationships between input and output variables. In addition, ANFIS shows potential in processing diverse environmental data and offers higher accuracy than alternative methods such as ANN, SVM, and GP. SVM excels at efficiently modelling complex relationships and heterogeneous data. Meanwhile, DL methods, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), are crucial in improving prediction accuracy at different temporal and spatial scales. GP methods have also shown promise in modelling complex and nonlinear relationships in groundwater data, providing more accurate and reliable predictions when combined with optimisation techniques and uncertainty analysis. Therefore, integrating these methods and optimisation techniques (Hybrid Algorithms), tailored to specific hydrological and hydrogeological conditions, can significantly increase the predictive capability of GWL models and improve the planning and management of water resources. These findings emphasise the importance of thoroughly understanding (a priori) the functionalities and capabilities of each potentially beneficial AI-based methodology, along with the knowledge of the physical characteristics of the territory under investigation, to optimise GWL predictive models.

A probabilistic model for real-time quantification of building energy flexibility

Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.

Number- and size-controlled rainfall regimes in the Netherlands: physical reality or statistical mirage?

Abstract. An experimental study aimed at identifying special rainfall regimes with the help of co-located disdrometers is performed. Eight potentially special events (i.e., four number-controlled events and four size-controlled events) are identified and examined. However, a detailed cross-check with additional, independent radar measurements reveals no clear evidence of special rainfall dynamics. The research underscores the difficulty of experimentally confirming seemingly straightforward questions about rainfall patterns and dynamics that have been theorized in the literature for several decades but never formally validated experimentally. The study also questions the reliability of previous claims and serves as a reminder to approach such problems with more caution, emphasizing the need for rigorous uncertainty analysis and multiple cross-checks between sensors to avoid misinterpretation.

Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization

Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.

Optimizing active distribution microgrids with multi-terminal soft open point and hybrid hydrogen storage systems for enhanced frequency stability

The recent increased interest in active distribution microgrids has complicated the process of reaching stable generation with the presence of distributed generators (DGs). These challenges can be handled by employing multi-terminal soft open points (SOPs), which can further boost system performance compared to the conventional two-terminal SOP, especially with present system disturbances. At the same time, hydrogen energy storage has drawn increased attraction to strengthen power grid stability and flexibility. This paper uses a hybrid-based energy storage device that employs an electrolyzer and fuel cell means with a hydrogen tank to absorb or generate power through multi-terminal SOP based on desired grid requirements. This work also offers a novel implementation of a hybrid jellyfish search and particle swarm optimization (HJPSO) applied to model predictive controllers (MPCs) to provide a solution regarding frequency control issues for multiple microgrids comprised of hybrid micro-turbines and renewable generators with the presence of storage devices. Furthermore, different forms of nonlinearity and actual data measurements for the implemented renewable generators are incorporated to attain further realistic analysis. The transient behavior of the studied microgrid is substantially augmented via the suggested control scheme. Referring to the modern grid codes and standards, the frequency operation limits in a specific range. This requirement has been preserved throughout the entire simulations, including uncertainty analysis, assessing the capabilities of multi-terminal SOP in providing frequency disturbance ride-through for the operating active hybrid distribution microgrid under various operating conditions.

Risk Mitigation in Development Drilling Through Improved History Matching by Incorporating Uncertainty Analysis with a Hidden Gas Cap

Risk Mitigation in Development Drilling Through Improved History Matching by Incorporating Uncertainty Analysis with a Hidden Gas Cap

Uncertainty analysis for free-space three-dimensional measurement of electromagnetic pulse

As the polarization direction is unknown in free-space three-dimensional (3D) electromagnetic pulse (EMP) measurement, the components in three directions are usually measured first, and then the total vector is calculated. The waveform (magnitude) and polarization direction define the 3D process. Because of the uncertainty produced in the component measurement, there is also uncertainty in the calculated 3D process. This paper investigates the propagation of uncertainty during the total vector calculation process. The magnitude and polarization angle uncertainty propagation formulas are derived through analysis. The results show that the uncertainty of the calculated magnitude is less than the maximal measurement uncertainty of the three components, and the uncertainty of the polarization angle is less than sqrt(2) times the maximal uncertainty of the three components divided by the magnitude of the measured field. Finally, a Monte-Carlo (MC) simulation is run to validate the results of the analysis. The simulation results agree well with the analysis results.

Evaluation of the potential impact and cost-effectiveness of respiratory syncytial virus (RSV) prevention strategies for infants in Argentina

Background. New interventions are available for the prevention of respiratory syncytial virus (RSV) disease in young infants. We aimed to assess the potential impact and cost-effectiveness of using a long-acting monoclonal antibody (RSV mAb) or maternal RSV vaccine in the Argentine context. Methods. We used a static proportionate outcomes model to calculate the costs and consequences of using RSV mAb or maternal RSV vaccine over a ten-year period (2025–2034) in Argentina, assuming both year-round and seasonal administration. We compared each intervention to no pharmaceutical RSV intervention. The primary outcome was the discounted cost per disability-adjusted life year (DALY) averted from a societal perspective. We assumed willingness-to-pay of US$ 12,285 per DALY averted (0.9 times the national gross domestic product per capita). We used population study data on costs and disease burden and the efficacy of clinical trials of both interventions as inputs. We ran deterministic and probabilistic uncertainty analyses. Findings. Either strategy (RSV mAb or maternal RSV vaccine) could prevent >25% of RSV deaths aged <5 years and ∼30% aged <6 months (the age group where most intervention impact occurs). With a dose price of $US 50, both products have a 100% probability of being cost-effective compared to no intervention (US$ 5283 [95%CI $5203–$5363] and US$ 5522 [95%CI $5427 – $5617] per DALY averted for year-round use of RSV mAb and maternal RSV vaccine, respectively). Similar health impact could be achieved by a six-month seasonal strategy, which could improve cost-effectiveness by around 45% (assuming the dose price is unchanged). Interpretation. Either RSV mAb or maternal RSV vaccine are worth consideration in Argentina when priced at ≤US$ 50 per dose. A seasonal strategy could improve cost-effectiveness.

The MUSIC Critical Benchmark and Nuclear Data

The Measurement of Uranium Subcritical and Critical (MUSIC) experiment was a series of measurements of critical and subcritical configurations of bare highly enriched uranium. The goal was to compare measurement methods, analysis techniques, and simulation methods across regimes of criticality and to provide high-quality validation of 235U nuclear data. A benchmark evaluation of the two critical configurations of the MUSIC experiments will soon be published in the release of the International Criticality Safety Benchmark Evaluation Project Handbook. The recent execution of the experiment aids in proper quantification of model simplifications and all uncertainties associated with the experiment. Historical benchmark evaluations are heavily relied on for uranium nuclear data validation despite the fact that the same level of documentation and comparable uncertainty analysis may not be present. The MUSIC evaluation is less likely to include “unknown unknowns” that could impede accurately modeling the system. Presented are both highly detailed and very simplified models, which represent the experimental configurations accurately, aiding the users of the benchmark for nuclear data or transport code validation. The sensitivities of k eff to nuclear data and nuclear data–related uncertainties are very similar between this experiment and previous bare uranium sphere experiments. In addition, the nuclear data uncertainties to any nuclides other than 235 U are small. For all these reasons, the recently evaluated MUSIC benchmark critical configurations could prove very useful for 235 U nuclear data validation. Currently, major libraries have good agreement with the experimental results, within 200 pcm for all nuclear data libraries, and within one standard deviation of the experimental result for most. Suggested nuclear data adjustments based on MUSIC and Lady Godiva are also presented, with posterior improvements to both the agreement in k eff and the uncertainty associated with the nuclear data.

Production of phosphate biofertilizers as a booster for the techno-economic and environmental performance of a first-generation sugarcane ethanol and sugar biorefinery

The intensive use of fertilizers and pesticides in agriculture has a significant economic and environmental impact worldwide. Biofertilizers (aka microbial inoculants) could be a potential alternative to decrease costs and the environmental footprint linked to the use of fertilizers while boosting productivity through biological processes. This work aimed to perform a techno-economic-environmental assessment of an industrial biofertilizer production facility integrated with a sugarcane ethanol biorefinery. To this end, systems engineering tools were employed concurrently with techno-economic-environmental analyses to assess the integration of the different processes and their feasibility. Three processes for biofertilizer production are proposed varying in terms of downstream processing and the use of single or double microorganisms. Our findings indicate that the integration of biofertilizer production can enhance the biorefinery's NPV by as much as 137% in the most favorable scenario and by a minimum of 69% in the most unfavorable scenario. Regarding environmental consequences, in general, all scenarios demonstrate an improvement over the base scenario. Global sensitivity analysis showed that the solid-state fermentation and composite formulation steps of the biofertilizer process have the most substantial influence on both economic and environmental outcomes. The uncertainty analysis further unveils that the scenarios without fungus separation exhibited greater resilience in the face of market volatility. The retro-techno-economic study defined the economically viable region. Ultimately, this study demonstrates that the integration of biofertilizers into an ethanol and sugar biorefinery is a more sustainable alternative than the isolated biorefinery regarding the environmental and techno-economic aspects of sustainability.

Study on Life-Cycle Carbon Footprints and an Uncertainty Analysis of Mega Sporting Events: An Analysis in China

This study proposes a model for the quantitative evaluation of the life-cycle carbon footprints of large sporting events and the uncertainties related to them. The model was used to analyze the case of a mega sporting event in Beijing, China. First, the quantitative model for the evaluation of the carbon footprints of mega sporting events includes a preparation stage, a holding stage, and an end stage. These stages consider the energy and resources used for construction, operation, transportation, catering, and accommodation. Second, this study proposes a prediction model using model-based and simulation-based methods to address the difficulty of obtaining traffic activity. Third, a semi-quantitative method that combines a data quality indicator and stochastic simulation is adopted for the uncertainty analysis of mega sporting events. Finally, a case study is used to indicate that the preparation stage of a mega sporting event accounts for the highest CO2 emissions at 92.1%, followed by 7.5% in the holding stage and 0.4% in the end stage. The total life-cycle CO2 emissions of a sustainable scenario of a mega sporting event in Beijing amount to 205,080.3 t CO2e, and the per capita CO2 emissions during the event’s holding stage amount to 0.26 t CO2e/person. The uncertainty in the input parameters is 0.0617, indicating that the uncertainty of the model is low, and the reliability of the results is high.

Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction

Accurate prediction of photovoltaic power generation is essential to promoting the active consumption and low-carbon protection. The complex uncertainty of the photovoltaic system itself leads to the deviation in the photovoltaic power prediction. Therefore, we propose a new prediction model for coupled intelligence optimization. First, the photovoltaic power is decomposed into effective mode components using VMD optimized by GWO. Statistical techniques were used to analyze multidimensional uncertainty and extract features, then, optimize the performance of the coupled model. Second, the Zebra optimization (ZOA) establishes an appropriate balance between exploration and utilization to achieve the optimization of the model parameters. In addition, the CNN is used to extract complex features and enhance the correlation between input values and output values. Finally, the power was predicted using the BiLSTM. The results show that applying the statistical technique to the coupled prediction model not only reveals the uncertainty of photovoltaic systems but reduces the prediction error. Among them, the R2 increased by 0.42 %, the values of MAPE, MSE, RMSE, and MAE were reduced to different degrees. It can better optimize the allocation and reasonable consumption of renewable energy, which provides the decision basis for the adjustment of renewable energy structure.

Measurement of bidirectional transmittance distribution function in the visible and near-infrared spectral range

This work characterizes a facility for bidirectional transmittance distribution function (BTDF) measurements in the visible and near-infrared (NIR) wavelength range. The facility includes an absolute reference gonioreflectometer, with an extended capability for BTDF measurements, and a commercial instrument, Cary 7000, using a goniometer extension, Universal Measurement Accessory. The facility characterization includes measurements of two quasi-Lambertian diffusers for their BTDF. The diffuser samples include a piece of porous polytetrafluoroethylene (PTFE) and a piece of fused synthetic silica, HOD-500. The samples are measured for their spectral BTDF in the wavelength range from 450 nm to 1650 nm in 50 nm steps, and in the viewing zenith angle range from −35° to 35° in 5° steps. Measurements are performed in-plane, using an incident zenith angle of 0°, while rotating the samples around their surface normal from 0° to 180° in 90° steps. Rotation of the sample serves the dual purpose of checking the sample rotational symmetry in azimuth angles and for averaging the measurement results. The characterization results show that both samples exhibit Lambertian characteristics across the visible and NIR wavelength range. Furthermore, both samples show a smooth increase in their spectral BTDF as a function of wavelength. Notably, the PTFE sample demonstrates a steeper slope in its spectral BTDF and a higher signal above 650 nm. The facility capability for measurements of spectral BTDF is validated by a rigorous uncertainty analysis and by comparing the difference in measurement results between the absolute gonioreflectometer and the commercial instrument. Characterization results indicate that the absolute reference gonioreflectometer has a standard uncertainty ranging from 0.29% to 0.42%, dependent on the BTDF measurement wavelength. Experimental results show that the devices of the facility deviate in their BTDF within their combined expanded uncertainty, affirming the capability and reliability of the facility for BTDF measurements.

Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas

Cropland soil organic matter (SOM) is recognized as a significant carbon reservoir in terrestrial ecosystems. Digital mapping of SOM in croplands is essential for comprehending the global carbon cycle. Accurately mapping cropland SOM using multi-source remote sensing data has been effectively incorporated into prediction models across various scales. However, the impact of multi-source remote sensing data on cropland SOM mapping outcomes in hilly and mountainous regions remains insufficiently understood. In this study, Jiangyou City, located in Sichuan Province, China, was chosen as a representative example of hilly and mountainous regions. Fifteen distinct feature combinations were devised using three remote sensing variables (Sentinel-1, Sentinel-2, and Landsat-8) along with DEM data. Feature selection was conducted using the Boruta algorithm. Subsequently, the RF, SVR, Cubist, and INLA-SPDE models were adopted to create spatially detailed distribution maps of cropland SOM for the region. Additionally, an uncertainty analysis was performed on the cropland SOM mapping results. The results indicate the following: (1) The INLA-SPDE model, which integrates both data information and spatial structure, achieves the highest accuracy and the less uncertainty in cropland SOM mapping, with an R2 of 0.647 and an RMSE of 4.227 g/kg. (2) Optical imagery is more important than SAR images, but their combination enhances model accuracy. Specifically, Sentinel-2 data has a significant impact cropland SOM prediction in hilly and mountainous areas, followed by Landsat-8 data. (3) The predicted spatial distribution patterns of cropland SOM by the four models show consistency, indicating lower SOM content in the southwest and higher SOM content in the central and northeast regions. This study provides valuable references for future large-scale and high-spatial cropland SOM prediction, highlighting the importance of spatial resolution for precise SOM prediction accuracy in hilly and mountainous regions.