Drivers of future streamflow changes in watersheds across the Northeastern United States

Accurate projections of streamflow, which have implications for flooding, water resources, hydropower, and ecosystems, are critical to climate change adaptation and require an understanding of streamflow sensitivity to climate drivers. The northeastern United States has experienced a dramatic increase in extreme precipitation over the past 25 years; however, the effects of these changes, as well as changes in other drivers of streamflow, remain unclear. Here, we use a random forest model forced with a regional climate model to examine historical and future streamflow dynamics of four watersheds across the Northeast. We find that streamflow in the cold season (November–May) is primarily driven by 3-day rainfall and antecedent wetness (Antecedent Precipitation Index) in three rainfall-dominant watersheds, and 30-day rainfall, antecedent wetness, and 30-day snowmelt in the fourth, more snowmelt-dominated watershed. In the warm season (June–October), streamflow is driven by antecedent wetness and rainfall in all watersheds. By the end of the century (2070–2099), cold season streamflow depends on the importance placed on snow in the machine learning model, with changes ranging from −7% (with snow) to +40% (without snow) in a single watershed. Simulated future warm season streamflow increases in two watersheds (56% and 193%) due to increased precipitation and antecedent soil wetness, but decreases in the other two watersheds (−6% and −27%) due to reduced precipitation.     

边坡稳定性自动机器学习预测方法研究

为了简便有效地评估边坡稳定性状态,针对目前传统机器学习的算法选择与超参数优化等难题,提出1种边坡稳定性自动机器学习预测方法。首先,简要介绍5种主流开源自动机器学习框架;其次,以422组边坡稳定性样本为数据集,进行自动机器学习纯自动化训练,并与传统机器学习对比分析模型的性能与耗时;最后,综合讨论与比较典型自动机器学习框架的特性。研究结果表明：自动机器学习预测效果总体上优于传统机器学习,提升边坡稳定性预测准确率和稳健性,且无需人为干预。研究结果可为岩土工作人员准确可靠地评价边坡稳定性提供便捷条件。     

基于HMM和DT的无人机异常检测方法

为了实时检测无人机异常状态,提出基于隐马尔可夫模型(Hidden Markov Model,HMM)和决策树(Decision Tree,DT)的无人机异常检测方法(HMMDT).首先根据异常致因将无人机异常分为干扰异常和硬件异常;然后结合HMM和DT建立无人机异常检测模型,定义无人机异常度衡量异常状态的严重程度,确定...     

混凝土结构使役环境中温度智能模拟LabVIEW软件设计

本项目结合湖北省科技计划自然科学基金项目“混凝土结构使役环境的智能模拟”,在WTST-150试验机硬件平台基础上,以LabVIEW8．5为开发平台,设计了一套混凝土结构使役环境中温度智能模拟LabVIEW软件,主要包括能初始化程序、温度采集与显示子程序、温度PID控制子程序、温度存储和查询子程序。经投入实验验证,该软件能实现实时曲线显示、温度控制、试验状态和保存试验数据、数据查询、手动加热、分配温度通道、PID控制参数整定、制冷、读入温度曲线等功能。具有实时在线循环检测、采集数据、响应快速等优点,并具有良好的故障诊断能力和可维护性以及超强的纠错能力。本软件完善了WTST-150试验机的温度控制系统,使其更加符合实际工况。     

大型烧结机头电除尘器提效改造探讨

本文针对320m^2烧结机头电除尘器烟气、烟尘的特点,以及该电除尘器存在的关键问题,着重介绍在改造上采用新的除尘技术和方法,使改造后的电除尘器能够满足机头工艺除尘的要求,达到提效净化烟气达标排放的目的。     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

Building a freshman-year foundation for sustainability studies: Terrascope, a case study

Terrascope is a freshman learning community at the Massachusetts Institute of Technology (MIT) in which teams of students work to find solutions to large ‘unsolvable’ problems and to communicate about those problems with a wide variety of audiences in multiple formats. The program strongly promotes students’ autonomy in focusing and structuring their work, and student projects culminate in public presentations, both to general audiences and to panels of technical specialists. Students who have completed the program tend to show strong engagement with environmental and sustainability issues, as well as the skills and experience to work intensively on such issues within multidisciplinary teams. Here, we present the program as a case study, with some discussion of the factors that are key to its operation.

Learning for sustainability: participatory resource management in Cambodian fishing villages

Considerable attention is now being given to the role and importance of education and learning implications of participatory resource and environmental governance. The contention is that such learning has the potential to transform behaviour, and in the case of resource management, may help change current patterns in resource use towards a more sustainable social-ecological system. Our purpose is to establish the opportunities for learning afforded to those participating in local level resource management committees in two Cambodian fishing communities and consider the learning outcomes from such opportunities. Our approach was qualitative and used a case study design. The findings establish a wide range of committee resource management activities that resulted in both instrumental (e.g., learning about administrative procedures) and communicative (e.g., insights into the need to conserve mangroves) learning. It was revealed that such learning can lead to changes in behaviour at the community level (e.g., managing local waste). Our findings also indicated a number of motivators and enablers of learning such as opportunities for dialogue, leadership and the presence of declining resources.     

煤与瓦斯突出预测的支持向量机(SVM)模型

基于支持向量机(SVM)分类算法,考虑影响煤与瓦斯突出的主要因素,建立了煤与瓦斯突出预测的SVM模型。该模型选取开采深度、瓦斯压力、瓦斯放散初速度、煤的坚固性系数以及地质破坏程度5个指标作为模型输入量,同时将煤与瓦斯突出程度划分为无突出、小型突出、中型突出和大型突出4个等级,进而使其评判结果更为细化。以实测数据作为学习样本进行训练,建立相应判别函数对待判样本进行预测。通过算例分析,表明该模型的方法对煤与瓦斯突出预测的合理性与有效性,可以在实际工程中推广。     

企业安全管理体系的功能实现——生产安全问题确认和求解的视角

针对企业安全管理体系相关工作与日常安全管理之间存在的"两张皮"现象,首先借助企业生产安全"问题空间"和"解空间"的概念,探讨了企业安全管理的出发点和落脚点;然后,围绕企业具体安全问题的确认和求解,分析了如何在组织层面通过三阶"学习循环"推进体系功能的实现;最后,基于RACI模型探讨了如何在个体层面支持和响应组织层面的各阶"学习循环"以真正实现体系的功能。总之,"两张皮"现象主要来自企业生产安全"目的"和"手段"不能有效对接,安全管理体系建设和功能实现应从辨识和解决企业实际问题入手。