Extended Fourier Amplitude Sensitivity Test Research Articles

Sensitivity analysis on transient pressure of entrapped air pocket in deep stormwater tunnel system

In deep stormwater tunnel systems (DSTSs), entrapped air pockets are prone to pressurization and deformation during rapid filling, resulting in pressure surges that threaten system security. This study investigates the effects of structural and inflow parameters on peak pressures. To simulate the pressure surges of entrapped air pockets, a rigid-column model was developed for a simplified DSTS configuration comprising two shafts and one tunnel. The global sensitivities of the air pocket, system structure, and inflow parameters were calculated using the Extended Fourier Amplitude Sensitivity Test (EFAST), which employed indicators of the maximum pressure and relative increment. The results indicate that parameters related to the shape and initial state of the air pocket exert a substantial and direct effect on pressure surges in rapid filling, whereas tunnel system parameters tend to exhibit minimal influence. Notably, compared to the total inflow discharge, the flow difference between two shafts imposes a more significant and direct impact. For the maximum pressure of air pocket, the initial pressure and the maximum water level height of shafts demonstrate more pronounced effects. The proposed sensitivity analysis could be integrated into methodologies for system safety assessment, while the rigid-column model may be extended to accommodate multiple shafts and air pockets.

Coupling the PROSAIL Model and Machine Learning Approach for Canopy Parameter Estimation of Moso Bamboo Forests from UAV Hyperspectral Data

Parameters such as the leaf area index (LAI), canopy chlorophyll content (CCH), and canopy carotenoid content (CCA) are important indicators for evaluating the ecological functions of forests. Currently, rapidly developing unmanned aerial vehicles (UAV) equipped with hyperspectral technology provide advanced technical means for the real-time dynamic acquisition of regional vegetation canopy parameters. In this study, a hyperspectral sensor mounted on a UAV was used to acquire the data in the study area, and the canopy parameter estimation model of moso bamboo forests (MBF) was developed by combining the PROSAIL radiative transfer model and the machine learning regression algorithm (MLRA), inverted the canopy parameters such as LAI, CCH, and CCA. The method first utilized the extended Fourier amplitude sensitivity test (EFAST) method to optimize the global sensitivity analysis and parameters of the PROSAIL model, and the successive projections algorithm (SPA) was used to screen the characteristic wavebands for the inversion of MBF canopy parameter inversion. Then, the optimized PROSAIL model was used to construct the ‘LAI-CCH-CCA-canopy reflectance’ simulation dataset for the MBF; multilayer perceptron regressor (MLPR), extra tree regressor (ETR), and extreme gradient boosting regressor (XGBR) employed used to construct PROSAIL_MLPR, PROSAIL_ETR, and PROSAIL_XGBR, respectively, as the three hybrid models. Finally, the best hybrid model was selected and used to invert the spatial distribution of the MBF canopy parameters. The following results were obtained: Waveband sensitivity analysis reveals 400–490 and 710–1000 nm as critical for LAI, 540–650 nm for chlorophyll, and 490–540 nm for carotenoids. SPA narrows down the feature bands to 43 for LAI, 19 for CCH, and 9 for CCA. The three constructed hybrid models were able to achieve high-precision inversion of the three parameters of the MBF, the model fitting accuracy of PROSAIL_MLRA reached more than 95%, with lower RMSE values, and the PROSAIL_XGBR model yielded the best fitting results. Our study provides a novel method for the inversion of forest canopy parameters based on UAV hyperspectral data.

Precision forecasting of fertilizer components’ concentrations in mixed variable-rate fertigation through machine learning

Accurate monitoring of fertilizer concentration and variable irrigation components is crucial for achieving precision irrigation through variable-rate fertigation. However, technological and cost constraints pose challenges in effectively managing mixed variable-rate fertigation. This study addresses these challenges by integrating machine learning (ML) with easily monitored physical parameters, such as electrical conductivity (EC), pH, and temperature, to predict fertilizer solution components and concentrations in mixed variable-rate fertigation. Cubic spline interpolation (CSI) was used to enhance the training dataset. Six ML algorithms—Multivariate Linear Regression (MLR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETs), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGB)—were used to develop prediction models, with their performance evaluated using the coefficient of determination (R2), root mean square error (RMSE), and Akaike information criterion (AIC). The Extended Fourier Amplitude Sensitivity Test (EFAST) assessed the sensitivity of the ML models to physical parameters. All of the ML models, except for MLR, particularly SVM, demonstrated superior performance in predicting fertilizer solution components’ concentrations, with R2 values between 0.989 and 0.997 and RMSE values between 0.089 and 0.210. The CSI significantly enhanced model performance, resulting in larger R2 values and smaller RMSE values. The AIC results and sensitivity analysis confirmed the exceptional performance of SVM, emphasizing its suitability for predicting fertilizer components using easily measured physical parameters. The developed ML models offer valuable insights for decision-makers managing irrigation and fertilization in mixed variable-rate fertigation.

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

How global sensitive is the AquaCrop model to input parameters? A case study of silage maize yield on a regional scale

IntroductionAquaCrop is a water-driven crop growth model that simulates aboveground biomass production in croplands. This study aimed to identify the driving parameters of the AquaCrop model for the model calibration and simplification to fill the research gap in intermediate environmental conditions between sub-tropical sub-humid and temperate sub-humid climates for silage maize.MethodsTo this end, we applied global sensitivity analysis (GSA) by combining the Morris method and the Extended Fourier Amplitude Sensitivity Test (EFAST) on crop yield output. The process involved a field sampling of soil and crop of silage maize carried out in the agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019 in order to measure certain model parameters.Results and discussionIn compliance with the Morris method, 30 parameters were identified as the least sensitive, while results from the EFAST test showed 9 parameters as contributing to the highest sensitivities in the model. The results clearly point to the capacity of employing a combination of both methods to attain a more efficient model calibration. Particular root, soil, canopy development, and biomass production parameters were influential and merit attention during calibration. Instead, parameters describing crop responses to water stress were acting rather insensitive in this study condition. The insights gained from this study, i.e., assessing parameter ranges and distinguishing between less sensitive and more sensitive parameters based on environmental and crop conditions, have the potential to be applied to other crop growth models with caution.

Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model

Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.

Mathematical Modeling of Alopecia Areata: Unraveling Hair Cycle Dynamics, Disease Progression, and Treatment Strategies

Abstract This paper describes the model development process in detail, including the formulation of equations and parameters based on existing knowledge of hair cycle dynamics and immune system interactions. Various analyses are conducted to gain insights into the behavior of the model. Illustrative simulations are performed to observe the temporal dynamics of the disease progression under different conditions. Sensitivity analysis using eFAST (Extended Fourier Amplitude Sensitivity Test) is employed to identify the most influential parameters affecting the length of the anagen phase in hair follicles affected by alopecia areata. The findings of the study shed light on the complex dynamics of alopecia areata and contribute to a deeper understanding of the disease mechanisms. The model provides a valuable tool for studying autoimmune hair loss diseases and may have implications for the diagnosis and treatment of such conditions. By simulating the immune response and its effects on hair follicles, the model offers insights into potential treatment strategies that can target immune dysregulation. The temporal mathematical model presented in this dissertation provides a comprehensive framework for investigating alopecia areata and understanding its underlying dynamics. The integration of hair cycle dynamics and immune system interactions enhances our knowledge of the disease and opens avenues for future advancements in diagnosis and treatment approaches.

Sensitivity analysis of simulated Lycium barbarum L. yield in the WOFOST model under different climate conditions

Calibrating process-based crop models in a timely manner is often challenging due to the extensive range of parameters involved. Sensitivity analysis (SA) is a useful practice for analyzing model uncertainty in these parameters. In this study, the Morris and extended Fourier Amplitude Sensitivity Test (E-FAST) methods were employed in sub-model level experiments. The yield outputs of WOFOST model under different climate conditions were used to evaluate the impact of altering model parameters in the modeling of Lycium barbarum L. The results indicate that parameters associated with CO2 assimilation rate, leaf area expansion and thermal time during specific periods have a significant impact on the simulated yield. The sensitive parameter rankings obtained from E-FAST exhibited good concordance across each planting site, and both SA methods consistently revealed similar rankings. This study provided a strategy to efficiently verify the applicability of new crop model parameters in different regions.

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored.

Application of the extended Fourier amplitude sensitivity testing (FAST) method to inflated, axial stretched, and residually stressed cylinders

Application of the extended Fourier amplitude sensitivity testing (FAST) method to inflated, axial stretched, and residually stressed cylinders

Control strategy of solid oxide electrolysis cell operating temperature under real fluctuating renewable power

Power-to-liquid (PtL) technology through solid oxide electrolysis cells (SOECs) can efficiently convert renewable energy sources into synthetic fuels for long-time energy storage. However, due to its fluctuation and intermittence, renewable energy sources only produce time-varying currents, which directly impact the operating temperature of SOEC, generating high thermal stresses for SOEC and reducing its durability. To deal with this issue, a control-oriented SOEC dynamic model containing an electrochemical part, a reversible water gas shift (RWGS) part, and a thermodynamic part, was developed in this paper. Afterward, main control parameters, able to adjust SOEC operating temperature effectively, were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST). The corresponding results show that the air flow rate was the best choice to control SOEC temperature when it works in a low current density case, while the fuel flow rate becomes optimal when SOEC operates in a high current case. Given this knowledge, an adaptive feedback control strategy was proposed to stabilize the operating temperature of SOEC in practice. In the end, the effectiveness of the proposed control was demonstrated through step signals of current in both low- and high-current density cases and an actual fluctuating current from real solar cells.

Sensitivity Analysis of the Land Surface Characteristic Parameters in Different Climatic Regions of the Loess Plateau

Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi-Parameterization (Noah-MP) land surface model. Sensitivity analysis can better elucidate the influence of different parameters on the model simulation results and evaluate the rationality of each model parameter. The extended Fourier amplitude sensitivity test (EFAST) method is a classical global sensitivity analysis method, whose theory is derived from the analysis of variance and Fourier transform. In this study, the EFAST method was used to perform sensitivity analyses on the land surface characteristic parameters in different climatic regions of the Loess Plateau. The results showed that the Noah-MP model can represent the land surface characteristics of the Loess Plateau well. With sensible and latent heat fluxes as criteria, the main sensitivity parameters were the vegetation roughness length (Z0), the soil quartz content (QUARTZ), the maximum volumetric soil moisture (MAXSMC), and the soil parameter “b”. The coupling effect between parameters has a greater impact on the sensitivity analysis. The probability densities of the three most sensitive parameters were evenly distributed in each interval, whereas those of the other parameters were distributed within 0–0.2 of the standardized value. Moreover, almost half of the land surface parameters accounted for 80% of the total sensitivity. Based on the seasonal sensitivity distribution of the land surface parameters, Z0 dominated throughout all four seasons, QUARTZ sensitivity was high in spring, and both MAXSMC and QUARTZ showed high sensitivities in winter.

Integrating multi-influencing factor techniques and fuzzy methods to identify recommendation domains for out-scaling conservation agriculture in China

Climate-smart agriculture (CSA) is a global development strategy aimed to address the interlinked challenges of food security and climate change. Expanding the implementation of conservation agriculture (CA), a vital component of CSA, is essential for enhancing agricultural and food security resilience while sustainably managing arable land. However, the extensive heterogeneity of biophysical and socioeconomic conditions presents significant complexities in promoting CA adoption. Addressing these challenges, this study carried out a comprehensive theoretical investigation of biophysical and socioeconomic factors influencing CA adoption and performance, integrating stakeholder feedback to create a systematic and robust evaluation index system for assessing CA suitability. By integrating multi-influencing factor techniques and fuzzy logic methods, we spatially identified suitable areas for CA implementation in China, providing valuable insights for land use policy. The reliability of the models was verified through a sensitivity analysis using the map removal sensitivity analysis method and the extended Fourier amplitude sensitivity test. The results indicated that 29.78% of the cultivated land was unsuitable or marginally suitable for CA, while 29.30 and 40.92% were determined to be moderately suitable and suitable zones, respectively. Suitable cultivated land was primarily distributed in the northern arid and semi-arid regions, the Loess Plateau, the Huang-Huai-Hai Plain, and the Northeast China Plain. Conversely, unsuitable, and marginally suitable cultivated land was predominantly located in the Qinghai Tibet Plateau, Middle-lower Yangtze Plain, Sichuan Basin and surrounding areas, the Yunnan-Guizhou Plateau, and Southern China. The topographical index, annual mean precipitation, humidity index, and population density were identified as the most significant factors influencing CA suitability. The CA suitability maps generated in this study will guide the development and extension agents targeting CA to suitable locations with a high potential impact, thereby maximizing the likelihood of adoption and minimizing the risk of failure.

Benefit of aerosol reduction to winter wheat during China's clean air action: A case study of Henan Province

A strongly declining aerosol radiative effect has been observed in China since 2013 after implementing the clean air action, yet its impact on wheat (Triticum aestivum L.) production remains unclear. We use satellite measures and a biophysical crop model to assess the impact of aerosol-induced radiative perturbations on winter wheat production in the agricultural belt of Henan province from 2013 to 2018. After calibrating parameters with the extended Fourier Amplitude Sensitivity Test (EFAST) and the generalized likelihood uncertainty estimation (GLUE) method, the DSSAT CERES-Wheat model was able to simulate crop biomass and yield more accurately. We found that the aerosol negatively impacted wheat biomass by 21.87% and yield by 22.48% from 2006 to 2018, and the biomass effects from planting to anthesis were more significant compared to anthesis to maturity. Due to the strict clean air action, under all-sky conditions, the surface solar shortwave radiation (SSR) in 2018 increased by about 7.08% over 2006-2013 during the wheat growing seasons. As a result of the improvement of crop photosynthesis, winter wheat biomass and yield increased by an average of 5.46% and 2.9%, respectively. Our findings show that crop carbon uptake and yield will benefit from the clean air action in China, helping to ensure national food and health security.

A novel global snow depth retrieval method considering snow metamorphism and forest influence

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (fforest) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec, which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec, vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, fforest, elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales.

Global sensitivity analysis of fan coil air conditioning demand response—A case study of medium-sized office buildings

Air-conditioning demand response has drawn more attention as a means of reducing the ever-increasing peak load of the utility grid. However, the parametric analysis of the demand response performance is still insufficient, given a large number of design parameters of buildings, air-conditioning systems, climate zones, and demand response scenarios. In this study, four different sensitivity analysis methods, including the standardized regression coefficient method, the partial correlation coefficient method, the Morris method, and the extended Fourier amplitude sensitivity testing method, were adopted to screen the most important design parameters in determining the peak load, demand shedding intensity, and demand response potential of a medium-sized office building equipped with a fan-coil air conditioning system under the demand response control strategies of different global temperature adjustments. The results show that the four sensitivity analysis methods presented similar results of parameter screening, even with different degrees of accuracy and computational costs. For example, all the sensitivity analysis methods identified the occupied area per capita, fresh outdoor air per person, internal thermal mass, the safety factor of chiller capacity, and coefficient performance of chillers as the critical parameters for the demand shedding intensity. In addition, the critical parameters have similar rankings for their importance under different adjustment parameters, such as setpoint changes and duration time, in a demand response event. For critical parameters, their degrees of influence on demand shedding intensity are affected by climate zones, and the variation of coefficient for their sensitivity analysis indicators could be 20% higher than the least influential parameters. The study would benefit the design of air conditioning systems with load flexibility and prediction model simplification, etc.

Suitability Evaluation of Human Settlements Using a Global Sensitivity Analysis Method: A Case Study in China

The suitability evaluation of human settlements over time and space is essential to track potential challenges towards suitable human settlements and provide references for policy-makers. This study established a theoretical framework of human settlements based on the nature, human, economy, society, and residence subsystems. Evaluation indicators were determined with the consideration of the coupling effect among subsystems. Based on the extended Fourier amplitude sensitivity test algorithm, the global sensitivity analysis was used to determine the weights of indicators. The human settlement suitability was evaluated in 30 provinces of China between 2000 and 2016. The findings were as follows: (1) human settlement suitability index (HSSI) values increased significantly in all 30 provinces from 2000 to 2016. The suitability index of the residence subsystem in China exhibited the fastest growth, followed by the society and economy subsystems. (2) HSSI in eastern provinces with a developed economy was higher than that in western provinces with an underdeveloped economy. In contrast, the growth rate of HSSI in eastern provinces was significantly higher than that in western provinces. (3) The inter-provincial difference in HSSI narrowed down from 2000 to 2016. For subsystems, the difference narrowed down for the residence system, whereas it widened for the economy system. (4) The suitability of the nature subsystem has become a limiting factor for the improvement of human settlement suitability, especially in economically developed provinces, such as Beijing, Shanghai, and Guangdong. The results can be helpful to support decision-making and policy for improving the quality of human settlements in a broad nature, human, economy, society, and residence context.

Risk Assessment Model and Sensitivity Analysis of Ordinary Arterial Highways Based on RSR–CRITIC–LVSSM–EFAST

In this paper, in order to evaluate the traffic safety status of ordinary arterial highways, identify the sources of safety risks, and formulate safety development countermeasures for arterial highways to reduce accident risks, a combination method involving rank-sum ratio (RSR), criteria importance though intercriteria correlation (CRITIC), and least squares support vector machine (LVSSM) is adopted. The traffic safety risk index system and risk assessment model of ordinary arterial highways with two dimensions of risk severity and accident severity are established. Based on the global sensitivity analysis of the extended Fourier amplitude sensitivity test (EFAST), the resulting risk assessment model for ordinary arterial highways is proposed. Combined with the current traffic safety situation of ordinary arterial highways in Weinan City, Shaanxi Province, China, data collection and analyses were carried out from the perspectives of traffic operation status, personnel facilities management, road environment characteristics, and accident occurrence patterns. The results show that the risk level of ordinary arterial highways can be obviously divided into warning areas, control areas, and prompt areas. The proportion of roads through villages and the number of deceleration facilities belong to the highly sensitive indicators of the S107 safety risk, which need to be emphatically investigated. This analysis method based is on the RCLE (RSR-CRITIC-LVSSM-EFAST) risk assessment model and has high operability and adaptability. It can be adaptively divided according to the requirements of risk-level differentiation, and the road risk classification can be displayed more intuitively, which is conducive to formulating targeted improvement measures for arterial highway safety and ensuring the safe and orderly operation of arterial highway traffic.

Time series global sensitivity analysis of genetic parameters of CERES-maize model under water stresses at different growth stages

Sensitivity analysis (SA) is used to identify the effects of crop model input parameters on model results. Previous studies have indicated that the CERES-Maize model was difficult to calibrate under the different water stress conditions. A genetic parameters time-series sensitivity analysis is needed to guide parameter optimization. The objectives of this study were to: (i) comprehensively quantify the genetic parameters in CERES-Maize based on the extended Fourier amplitude sensitivity test. The sensitivity of CERES-Maize output variables was analyzed under different water stress conditions; (ii) determine the sensitivities of output variables to genetic parameters during the growth period. The results demonstrated that output variable sensitivity varied in response to different water stress conditions. The total sensitivity index (TSI) and time-dependent TSI of crop parameters were more sensitive than the first sensitivity index (FOSI) and time-dependent FOSI of crop genetic parameters. The sensitivities of two years (2013 and 2014) based on FOSI and time-series FOSI were consistent; some differences existed between simulations based on TSI and time-series TSI. Under different water management conditions, the sensitivity of phyllochron interval (PHINT) to biomass decreased earlier and faster when drought occurred in the early growth period (D1 and D2). The time series of sensitivity index was consistent with the CK treatment when drought happened in the later growth periods (D3 and D2). The two parameters of PHINT and thermal time from emergence to end of juvenile (P1) were most sensitive to leaf area index (LAI) when drought occurred in the early growth periods (D1 and D2). In addition to PHINT and P1, other parameters also had sensitivities for LAI when drought occurred in later growth periods (D3 and D4). Future studies should focus on the response of dynamic output variable to soil parameters and weather conditions over the growing season in order to calibrate and apply the CERES-Maize model.

Assessment of the parameter sensitivity for the ORYZA model at the regional scale - A case study in the Yangtze River Basin

While global sensitivity analysis (GSA) has been widely implemented for crop models, few studies have assessed the sensitivity of model parameters at the regional scale. To fully understand the overall sensitivity of parameters in a specific area, this study carried out a regional-scale global sensitivity analysis (RGSA) by selecting multiple stations through grids within the study area and integrated GSA results obtained by extended Fourier amplitude sensitivity test (EFAST) from these stations by statistical indicators to quantify parameter sensitivity at the regional scale. Taking the Yangtze River Basin (YRB) as the study area, RGSA was implemented for the ORYZA model for paddy rice under two water and nitrogen situations (WNCs) and four levels of number of stations for sensitivity analysis (NSSA). The results suggested that the applicable area for the results of sensitive and insensitive parameters partitioning, sensitivity ranks and sensitivity indices at a specific station were not larger than grid sizes of 8° × 8°, 4° × 4° and 2° × 2° around it, respectively. Thus, the NSSA needed for sensitive parameter identification, sensitivity ranking and sensitivity indices variability analysis at regional scale increased sequentially. It was recommended that a suitable number of stations should be chosen for analysis according to the sensitivity analysis purpose at the regional scale. Since WNC will affect the results of RGSA, such analysis was recommended to be performed separately for different WNCs. Highly sensitive parameters of the ORYZA model for paddy rice in YZB were screened out through RGSA, which were DVRJ, DVRP and RGRLMX. Model users can adopt the RGSA workflow used in this study for parameter sensitivity assessment of other crop models.