SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

环境因素数据质量审核方法研究

目的 解决大气环境试验站的气象数据报表存在数据缺失、数据超过边界、数据内部不一致、数据时间不一致等问题,采用数据质量审核技术找出缺失、可疑或错误的数据,对科研人员进行警示,使其能够追根溯源,分析存在问题的原因。方法 参照相关规程和标准,建立环境因素数据质量审核算法和审核流程,并使用计算机语言编程固化流程和算法,利用多线程算法对数据进行扫描,实现大批量气象数据的并行审核。结果 利用Python语言设计了环境因素质量控制软件,通过该软件的应用证明了该方法合理、有效,能够快速、准确地标注气象数据报表中存在质量问题的数据。利用软件统计了存在问题数据的数量和正确率。结论 通过加载数据测试,证明了环境因素数据质量审核方法和审核流程合理有效,利用设计的审核软件实现了气象数据报表快速审核,提高了审核效率,减少了科研人员的劳动强度,为进一步填补、修正和利用数据奠定了基础。     

Boosting large-scale river connectivity restoration by planning for the presence of unrecorded barriers

Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection.     

基于化工生产过程历史数据的报警优化方法研究

为了有效降低报警装置漏报率和误报率,基于化工生产的过程历史数据,探讨了报警优化的两种方法。以误报率和漏报率为优化指标,建立报警阈值目标函数,从最小化目标函数的角度出发,采用数值优化的方法进行报警阈值的优化设计。基于报警时长的变异系数,应用自适应报警延时方法来降低报警数量。实例研究表明,应用基于化工生产过程历史数据的方法,可有效减少无效报警数量,降低误报率和漏报率,提高报警质量,对于提高石化生产过程的安全性具有积极意义。     

基于链式多重插补的WOA-ELM煤与瓦斯突出预测模型

为了提高缺失数据下煤与瓦斯突出预测准确率,提出1种基于链式支持向量机多重插补(MICE＿SVM)的鲸鱼优化算法(WOA)-极限学习机(ELM)预测模型,以淮南朱集矿区为例,选取5个煤与瓦斯突出影响指标作为模型特征,采用提出的MICE＿SVM算法插补突出事故数据中缺失值,利用WOA优选ELM输入层权值及隐含层阈值,构建煤与瓦斯突出预测模型,将插补后数据用于WOA-ELM模型的训练与测试,并与其他模型的预测效果对比。研究结果表明：MICE＿SVM插补前、后的有突出数据预测准确率分别为83.02%,90.41%,MICE＿SVM显著提高了有突出预测准确率,对无突出和整体的预测准确率提高不明显;数据插补后WOA优化ELM对无突出、有突出和整体的预测准确率分别为97.94%,96.25%,96.48%,较优化前分别提高了5.79%,5.84%,5.55%,数据插补后WOA-ELM为最佳预测模型。     

USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA1

ABSTRACT: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.     

Spatial and temporal projected distribution of four crop plants in Egypt

This study focuses on the management of the local agroecosystems in order to adapt planting or sowing practices for the projected climate change scenarios. It is projected that there will be increased air temperature throughout all four seasons in the coming 100 years, from the southern towards the northern parts of Egypt. The objective of this study is to investigate the influence of that increased air temperature on the spatial and temporal distribution of four of the major economic crops in Egypt. The study species are cotton (Gossypium barbadense L., cv. Giza 89), wheat (Triticum aestivum L., cv. Gemiza 9), rice (Oryza stiva L., cv. Sakha 101) and maize (Zea mays L., cv. Hybrid 10). Optimum air temperature allowing maximum growth for each of the study crop cultivars and the current and projected air temperature patterns in the future years were used for projection of the seasonal and crop distribution maps in the years 2005, 2025, 2050, 2075 and 2100. Results showed that sowing dates of a target crop may be managed in order to allow maximum predicted planting area in the same region. The current maximum area suitable for planting the Cotton crop in Egypt (104 thousand Fadden/year; one Fadden = 0.96 hectare or 0.42 acre) showed few variations over the coming hundred years. In this case, the sowing dates should be changed from the hotter months (February to April) to the cooler months (January to February). Alternatively, a great reduction in the area planted by Wheat crop was predicted in the coming 100 years. Despite the early planting, a reduction of about 147 thousand Fadden/year was projected by the year 2075. On the other hand, with earlier sowing dates, the maximum areas that are planted by Rice and Maize may not be greatly affected by the projected increase in air temperature.     

我国部分城市和地区居民龋均与饮用水氟含量的关系研究

为探讨饮水氟化在我国的可行性及饮水氟含量的安全值,在全国牙痛防治指导组1995年全国城乡居民口腔健康流行病学调查资料的基础上,对我国部分城市和地区饮用水氟含量进行了检测,对饮用水氟含量与居民龋均数据进行相关性分析。结果显示,在安全的饮水氟浓度范围内,龋均与饮用水氟浓度之间存在负相关关系,表明适度饮水氟化可以有效降低龋均;当水氟浓度超过安全临界点时,龋均与饮用水氟浓度呈正相关关系,说明高氟含量饮用水不仅可以导致氟中毒流行,也会提高龋病的发病率。我国饮用水氟含量的适宜范围为0.8~1.0 mg／L。研究发现饮用水氟浓度与龋均之间没有形成很好的线性关系,说明饮用水氟含量并不是龋病的唯一控制因素(说明龋病受多因素控制)。     

二氧化碳的失汇

温室气体的大量排放所造成温室效应的加剧是全球变暖的基本原因。全球和区域碳循环已成为全球气候变化研究的重要内容之一。碳汇的巨大功能深受世人瞩目,影响碳汇增加因素的研究也越加必要。