Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.     

Do Embedded Roadway Lights Protect Sea Turtles?

Street lighting on coastal roadways is often visible at sea turtle nesting beaches, and disrupts the nocturnal orientation of hatchlings as they crawl toward the sea. Our objective was to determine whether an alternative lighting system (light-emitting diodes, embedded in the roadway pavement) prevented orientation disruption. Hatchlings at the beach oriented normally when only the embedded lights were on, or when all lighting was switched off. However, turtles showed poor orientation when street lighting was on. Measurements confirmed that street lighting was scattered to the beach, whereas embedded lighting was not. We conclude that embedded lighting keeps the beach dark and thus protects sea turtles. However, on two overcast evenings, lighting (“skyglow”) from nearby development, reflected by cloud cover to the beach, weakened hatchling orientation. Thus, both indirect (reflected) and direct sources of lighting negatively impact the turtles.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Light distribution in dynamic street lighting: Two experimental studies on its effects on perceived safety,prospect, concealment,and escape

The relationship between light and perceived safety at night is intuitively strong, yet theoretically and empirically its workings are largely unknown. Intelligent dynamic road lighting, which continuously adapts to the presence and behavior of users, can light the street only when and where it is needed. As such, it offers a solution to the energy waste and luminous pollution associated with conventional road lighting. With this innovation, however, new questions emerge about the effect of lighting on perceived safety. We need to consider not only how much lighting pedestrians need to feel safe, but also which parts of the street should be lit. In two experiments, we investigated the effect of different light distributions on perceived safety, and explored mediation by people's appraisal of three safety-related cues suggested in the literature: prospect (having an overview), escape (perceived escape possibilities), and refuge/concealment (perceived hiding places for offenders). Both experiments, one with stationary and one with walking participants, demonstrated that people prefer having light in their own immediate surroundings rather than on the road that lies ahead. This could be explained, partially, by changes in prospect, escape, and concealment. Against expectations, prospect was higher with lighting distributions in which participants' immediate surroundings, but not the more distant parts of the road, were most strongly lit.     

Lighting and discomfort in the classroom

Aspects of classroom lighting and décor that can promote discomfort and impair task performance through glare, and imperceptible 100 Hz flicker from fluorescent lighting, were examined in a sample of UK schools. In 90 classrooms, across eleven secondary schools and six local education authorities variables measured included flicker, illuminance at desks, and luminance of whiteboards. Results showed that 80% of classrooms are lit with 100 Hz fluorescent lighting that can cause headaches and impair visual performance. Mean illuminance (from excessive day- and artificial lighting) was in excess of recommended design illuminance in 88% of classrooms, and in 84% exceeded levels beyond which visual comfort decreases. Lighting could not be adequately controlled due to classroom design and infrastructure. Ceiling-mounted data-projectors directed at whiteboards mounted vertically on the wall resulted in specular reflection from the whiteboard, visible as a glare spot with luminance high enough to cause discomfort and disability glare. The intensity of the glare spot varied between different brands of whiteboard. Ambient lighting, needed for close work at pupils' desks, reduced image contrast. Venetian blinds in 23% of classrooms had spatial characteristics appropriate for inducing pattern glare. There was significant variation between schools and local authorities. These findings may provide insights into small-scale reports linking pupils' attainment, behaviour and learning to classroom lighting, and may also help explain some of the benefits of coloured overlays for pupils' reading.     

Regression modeling of streamflow, baseflow, and runoff using geographic information systems

Regression models for predicting total streamflow (TSF), baseflow (TBF), and storm runoff (TRO) are needed for water resource planning and management. This study used 54 streams with >20 years of streamflow gaging station records during the period October 1971 to September 2001 in Pennsylvania and partitioned TSF into TBF and TRO. TBF was considered a surrogate of groundwater recharge for basins. Regression models for predicting basin-wide TSF, TBF, and TRO were developed under three scenarios that varied in regression variables used for model development. Regression variables representing basin geomorphological, geological, soil, and climatic characteristics were estimated using geographic information systems. All regression models for TSF, TBF, and TRO had R(2) values >0.94 and reasonable prediction errors. The two best TSF models developed under scenarios 1 and 2 had similar absolute prediction errors. The same was true for the two best TBF models. Therefore, any one of the two best TSF and TBF models could be used for respective flow prediction depending on variable availability. The TRO model developed under scenario 1 had smaller absolute prediction errors than that developed under scenario 2. Simplified Area-alone models developed under scenario 3 might be used when variables for using best models are not available, but had lower R(2) values and higher or more variable prediction errors than the best models.     

Potential Applicability of Persuasive Communication to Light-Glow Reduction Efforts: A Case Study of Marine Turtle Conservation

Artificial lighting along coastlines poses a significant threat to marine turtles due to the importance of light for their natural orientation at the nesting beach. Effective lighting management requires widespread support and participation, yet engaging the public with light reduction initiatives is difficult because benefits associated with artificial lighting are deeply entrenched within modern society. We present a case study from Queensland, Australia, where an active light-glow reduction campaign has been in place since 2008 to protect nesting turtles. Semi-structured questionnaires explored community beliefs about reducing light and evaluated the potential for using persuasive communication techniques based on the theory of planned behavior (TPB) to increase engagement with light reduction. Respondents (n = 352) had moderate to strong intentions to reduce light. TPB variables explained a significant proportion of variance in intention (multiple regression: R ² = 0.54–0.69, P < 0.001), but adding a personal norm variable improved the model (R ² = 0.73–0.79, P < 0.001). Significant differences in belief strength between campaign compliers and non-compliers suggest that targeting the beliefs reducing light leads to “increased protection of local turtles” (P < 0.01) and/or “benefits to the local economy” (P < 0.05), in combination with an appeal to personal norms, would produce the strongest persuasion potential for future communications. Selective legislation and commitment strategies may be further useful strategies to increase community light reduction. As artificial light continues to gain attention as a pollutant, our methods and findings will be of interest to anyone needing to manage public artificial lighting.     

SUBJECTIVE RESPONSES TO COMPUTER SIMULATIONS OF URBAN ENVIRONMENTS

Simulations of urban or natural environments created by graphics computer software are increasingly being utilized in research and applied contexts, and pertinent techniques have become highly sophisticated. However, the quality and utility of such presentation means still need careful validation. This issue was addressed in a series of lab and field studies. In the study reported here, a simulation of a suburban environment was presented to respondents (n=147) in several variations to investigate the effects of lighting (day/sun, day/fog, night), personal shadow (yes/no) and sound (on/off) on perceived simulation quality. The contingencies with related aspects (such as comprehension, recollection and appreciation of the simulated environment) were investigated as well. Based on a comprehensive conceptual framework, a set of scales measuring relevant cognitive and affective aspects was employed. Main results are that simulations were perceived as valid and acceptable, that appraisals differ according to lighting and time-of-day conditions, and that provision of sound enhances the perceived quality of presentations. The findings elucidate which factors are crucial for further improving simulations and clarify the validity of computer simulations for assessing existing and future environments.     

Hierarchical modeling of seed variety yields and decision making for future planting plans

Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.     

压力传感器位移分析及疲劳寿命预测

完成了依据有限元疲劳分析为基础的传感器寿命预测研究工作。阐述压力传感器工作原理,定义影响系统寿命的参数组,既包含力学环境参数,亦包括材料属性、几何形式等结构参数。针对不同参数属性,依据疲劳强度计算需求,构建有限元数值计算模型;根据影响传感器寿命的传感单元单晶硅S-N(应力-循环)分布,完成变载荷输入条件下模型疲劳分析,依据数值计算结果完成该压力传感器寿命预测工作。结果表明:压力传感器使用寿命在7.068E8次数以上。本课题研究提出的新方法,摆脱了传统依靠试验完成多种材料组成结构体的疲劳分析及寿命预测窘境,具有通用性。     

气温和湿度在泸州市城区对臭氧浓度预测的运用研究

气温和湿度均为影响臭氧生成的要素,通过分析泸州市国控点数据,找出气温和湿度与臭氧浓度的关系,从而建立气温和湿度为变量的臭氧浓度预测模型.选取2019年泸州市主城区的气温和湿度数据,将气温按日最高温(Tmax)、日最低温(Tmin)和日平均气温(Tmean)划分,将湿度按四季划分,分别统计其与臭氧浓度的相关性.发现臭氧浓...     

HYDROLOGIC PARAMETERIZATION OF WATERSHEDS FOR RUNOFF PREDICTION USING SWAT1

ABSTRACT: The use of continuous time, distributed parameter hydrologic models like SWAT (Soil and Water Assessment Tool) has opened several opportunities to improve watershed modeling accuracy. However, it has also placed a heavy burden on users with respect to the amount of work involved in parameterizing the watershed in general and in adequately representing the spatial variability of the watershed in particular. Recent developments in Geographical Information Systems (GIS) have alleviated some of the difficulties associated with managing spatial data. However, the user must still choose among various parameterization approaches that are available within the model. This paper describes the important parameterization issues involved when modeling watershed hydrology for runoff prediction using SWAT with emphasis on how to improve model performance without resorting to tedious and arbitrary parameter by parameter calibration. Synthetic and actual watersheds in Indiana and Mississippi were used to illustrate the sensitivity of runoff prediction to spatial variability, watershed decomposition, and spatial and temporal adjustment of curve numbers and return flow contribution. SWAT was also used to predict stream runoff from actual watersheds in Indiana that have extensive subsurface drainage. The results of this study provide useful information for improving SWAT performance in terms of stream runoff prediction in a manner that is particularly useful for modeling ungaged watersheds wherein observed data for calibration is not available.     

A Statistical Sediment Yield Prediction Model Incorporating the Effect of Fires and Subsequent Storm Events1

Abstract: Alluvial fans are continuously being developed for residential, industrial, commercial, and agricultural uses in southern California. Development and alteration of alluvial fans need to consider the possibility of mud and debris flows from upstream mountain watersheds affected by fires. Accurate prediction of sediment yield (or hyper‐concentrated sediment yield) is essential for the design, operation, and maintenance of debris basins to safeguard properly the general populace. This paper presents a model for the prediction of sediment yields that result from a combination of fire and subsequent storm events. The watersheds used in this analysis are located in the foothills of the San Gabriel Mountains in southern California. A multiple regression analysis is first utilized to establish a fundamental statistical relationship for sediment yield as a function of relief ratio, drainage area, maximum 1‐h rainfall intensity and fire factor using 45 years of data (1938‐1983). In addition, a method for multi‐sequence sediment yield prediction under fire conditions was developed and calibrated using 17 years of sediment yield, fire, and precipitation data for the period 1984‐2000. After calibration, this model was verified by applying it to provide a prediction of the sediment yields for the 2001‐2002 fire events in southern California. The findings indicate a strong correlation between the estimated and measured sediment yields. The proposed method for sequence sediment yield prediction following fire events can be a useful tool to schedule cleanout operations for debris basins and to develop an emergency response strategy for the southern California region where plentiful sediment supplies exist and frequent fires occur.     

Lasso-Cox模型在个人信用风险评估中的应用

借鉴Lasso模型和Cox模型的优点创建Lasso-Cox模型,用以评估个人借款者的信用风险,并利用个人借款者的数据进行实证分析,比较不同模型的预测精度。结果表明:(1)单一Cox模型的预测准确度高于Logistic回归模型,而Lasso-Cox模型的预测精度达95.76%,远高于单一Cox模型。(2)考虑宏观环境对个人经济的影响,发现将宏观变量纳入模型后预测精度有明显的提升,其中Lasso-Cox模型预测精度高达98.88%。     

A Heat Dynamic Model for Intelligent Heating of Buildings

This article presents a heat dynamic model for prediction of the indoor temperature in an office building. The model has been used in several flexible load applications, where the indoor temperature is allowed to vary around a given reference to provide power system services by shifting the heating of the building in time. This way the thermal mass of the building can be used to absorb energy from renewable energy source when available and postpone heating in periods with lack of renewable energy generation. The model is used in a model predictive controller to ensure the residential comfort over a given prediction horizon.     

Limiting the impact of light pollution on human health, environment and stellar visibility

Light pollution is one of the most rapidly increasing types of environmental degradation. Its levels have been growing exponentially over the natural nocturnal lighting levels provided by starlight and moonlight. To limit this pollution several effective practices have been defined: the use of shielding on lighting fixture to prevent direct upward light, particularly at low angles above the horizon; no over lighting, i.e. avoid using higher lighting levels than strictly needed for the task, constraining illumination to the area where it is needed and the time it will be used. Nevertheless, even after the best control of the light distribution is reached and when the proper quantity of light is used, some upward light emission remains, due to reflections from the lit surfaces and atmospheric scatter. The environmental impact of this "residual light pollution", cannot be neglected and should be limited too. Here we propose a new way to limit the effects of this residual light pollution on wildlife, human health and stellar visibility. We performed analysis of the spectra of common types of lamps for external use, including the new LEDs. We evaluated their emissions relative to the spectral response functions of human eye photoreceptors, in the photopic, scotopic and the 'meltopic' melatonin suppressing bands. We found that the amount of pollution is strongly dependent on the spectral characteristics of the lamps, with the more environmentally friendly lamps being low pressure sodium, followed by high pressure sodium. Most polluting are the lamps with a strong blue emission, like Metal Halide and white LEDs. Migration from the now widely used sodium lamps to white lamps (MH and LEDs) would produce an increase of pollution in the scotopic and melatonin suppression bands of more than five times the present levels, supposing the same photopic installed flux. This increase will exacerbate known and possible unknown effects of light pollution on human health, environment and on visual perception of the Universe by humans. We present quantitative criteria to evaluate the lamps based on their spectral emissions and we suggest regulatory limits for future lighting.     

The Design of a New Energy-conservation Type Solar-powered Lighting System Using a High‐pressure Sodium Lamp

This study designs and applies a new energy-conservation type solar-powered lighting system using a high-pressure sodium lamp to areas not having any utility company's electricity. The proposed system uses a zero-voltage-switching (ZVS) DC/DC converter in the batteries’ charge circuit to reduce the switching loss for a higher charging efficiency. Said system also adopts the maximum power point tracking (MPPT) technique to maximize the solar panels’ photovoltaic conversion capability. When dark, the batteries in the proposed system will discharge, with a raised voltage, through a push-pull DC/DC converter; said voltage, as the input voltage of the series-parallel resonant inverter, will be regulated to dim the lamp. To enable the efficient usage of the batteries’ stored energy capacity, this control scheme of the proposed system may adjust the night-time discharge time lengths, according to season difference, and compute the usable capacity for the load, according to the batteries’ charged voltage, so as to select a suitable pre-scheduled light-dimming curve for the lamp to achieve energy conservation for the batteries and continuity in lighting when dark.     

The Regionalization of National‐Scale SPARROW Models for Stream Nutrients1

Schwarz, Gregory E., Richard B. Alexander, Richard A. Smith, and Stephen D. Preston, 2011. The Regionalization of National‐Scale SPARROW Models for Stream Nutrients. Journal of the American Water Resources Association (JAWRA) 47(5):1151‐1172. DOI: 10.1111/j.1752‐1688.2011.00581.x Abstract: This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national‐scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national‐scale and regional‐scale information to estimate a hybrid model that imposes cross‐region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.     

Repeatability,Sensitivity, and Uncertainty Analyses of the BANCS Model Developed to Predict Annual Streambank Erosion Rates

Accelerated streambank erosion caused by channel instability can be the leading cause of sediment impairment of streams. Obtaining accurate streambank erosion rates for sediment budgeting and prioritizing mitigation efforts can be difficult and costly. One approach to quantifying streambank erosion rates is through the development and implementation of an empirically derived “Bank Assessment for Non‐point Source Consequences of Sediment” (BANCS) model. This study aims to improve the BANCS model application by evaluating repeatability between users and identifying sensitive and/or uncertain model inputs. Statistical analysis of streambank evaluations conducted by 10 different individuals suggests the implementation of the BANCS model may not be repeatable. This finding may be due to sensitive model inputs, such as streambank height and near‐bank stress level prediction method selection, and/or uncertain model inputs, such as bank material identification and the associated adjustment of erosion potential. Furthermore, it was found assessing streambanks as a group by obtaining a measure of central tendency from individual evaluations, as well as obtaining a higher level of training, may improve model implementation precision. Application of these suggestions may result in improved prediction of streambank erosion rates utilizing the BANCS model methodology.