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31.
Crowd-based hydrological observations can supplement existing monitoring networks and allow data collection in regions where otherwise no data would be available. In the citizen science project CrowdWater, repeated water level observations using a virtual staff gauge approach result in time series of water level classes (WL-classes). To investigate the quality of these observations, we compared the WL-class data with “real” (i.e., measured) water levels from the same stream at a nearby gauging station. We did this for nine locations where citizen scientists reported multiple observations using a smartphone app and at 12 locations where signposts were set up to ask citizens to record observations on a paper form that could be left in a letterbox. The results indicate that the quality of the data collected with the app was better than for the forms. A possible explanation is that for each app location, a single person submitted the vast majority of the observations, whereas at the locations of the forms almost every observation was made by a different person. On average, there were more contributions between May and September than during the other months. Observations were submitted for a range of flow conditions, with a higher fraction of high flow observations for the locations were data were collected with the app. Overall, the results are encouraging for citizen science approaches in hydrology and demonstrate that the smartphone application and the virtual staff gauge are a promising approach for crowd-based water level class observations.  相似文献   
32.
地表覆盖分类成果质量特征分析   总被引:1,自引:1,他引:0  
商建伟 《测绘通报》2020,(9):159-161
自我国全面建成地理国情普查成果库之后,工作重心由全面普查变为重点监测。不论普查还是监测,准确地对地表覆盖进行分类一直是工作的重点和难点。在常态化监测阶段,把握地表覆盖分类成果的主要质量指标,归纳其诸如变化率、变化区域分布、变化类型,分析影响其成果质量的主要因素,对监测生产组织及质量控制具有非常重要的作用。  相似文献   
33.
土地增减挂钩项目的质量检查的传统流程需到实地逐一核实,检查周期长。本文提出一种基于无人机航摄的检查技术流程,结合研发的野外实地调绘软件系统,提出一种新型的基于无人机航摄的增减挂钩项目质量检查流程,并基于DOM检查构建指标评价体系。流程优化了传统外业检查流程,实现地块的全覆盖内业检查和外业的实时取证,确保成果耕地质量指标的准确性,对土地增减挂钩项目给予客观评价。评价体系的应用选取广西几个县增减挂钩为研究对象,探究体系评价的合理性,初步确定该方法基本可行。  相似文献   
34.
ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.  相似文献   
35.
Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations.  相似文献   
36.
以艾比湖流域主要入湖河流为研究对象,在5月(丰水期)和8月(枯水期)分别沿博尔塔拉河(博河)和精河进行采样,采用平行因子模型(PARAFAC)和三维荧光区域积分法对水体三维荧光特性进行研究并对其与水质的关系在枯、丰水期下的变化进行探讨.结果表明①河流DOM在枯水期与丰水期都含有C1(240、425 nm) UVC类腐殖质,C2(225、290 nm)紫外区内络氨酸类有机物,C3(230/280、330 nm)蛋白类有机物,C4(265、260 nm)腐殖质类共4种组分.通过对水体三维荧光进行区域积分可以看出DOM荧光成分的占比在不同时期的变化.博河在枯水期时EEM光谱中的区域Ⅲ富里酸含量低于丰水期,枯水期时区域Ⅱ芳香类蛋白质、区域Ⅳ可溶性微生物代谢物以及区域Ⅴ类腐殖质酸高于丰水期;对于精河来说,区域Ⅱ芳香类蛋白质和区域Ⅳ可溶性微生物代谢物在枯水期的含量高于丰水期,区域Ⅲ富里酸和区域Ⅴ类腐殖质酸的含量枯水期低于丰水期,这表明水体腐质化程度较高.②本研究选取了一些常规的荧光指数来描述枯、丰水期水体的荧光指数特性.经研究发现,精河的荧光指数、自生源指数和腐殖化指数在不同时期的变化幅度较小,而博河的变化幅度较大.③将荧光指数与水质参数进行相关性分析并建模,结果表明枯水期自生源指数(BIX)与化学需氧量呈显著正相关,相关系数R=0.688;丰水期时BIX与铵态氮浓度呈显著负相关,相关系数R=-0.493.通过对比分析艾比湖主要入湖河流的三维荧光光谱特性与水质在枯、丰水期时的关系进一步表明水体中DOM的特性以及在枯、丰水期下的差异,为艾比湖流域的治理改善提供一定的理论支持和参考依据.  相似文献   
37.
This study used the Water Quality Analysis Simulation Program (WASP) to simulate nutrients, dissolved oxygen (DO), and chlorophyll-a dynamics in the Shenandoah River basin and performed an uncertainty analysis to examine the complexity of these variables in water quality estimation and their influence on the Shenandoah River. Significant progress has been made; however, nutrient loads emitted into the Shenandoah River from nonpoint sources remain high. Modeling of three points on the Shenandoah River in Virginia and West Virginia provides an ideal case study since the river is classified by the Virginia Department of Environmental Quality as being impaired. The results of a sensitivity test show that model error decreases with increasing model complexity and sensitivity. The model predicted DO values that tended to be close to the measured data, while total nitrogen and phosphorus tended to be overemphasized. Our results examine the importance of temperature, stream flow, and velocity in influencing water quality between seasons and levels on the different sections of the watershed.  相似文献   
38.
2020年7月12日,河北唐山发生MS5.1地震,河北省地震预警系统成功地处理并产出了这次地震预警各种结果数据,本文借助此次地震对河北地震预警网内震中距200km范围内台站产出质量以及地震预警前5次处理结果进行详细分析。此次地震发生在河北地震预警网内,平均台间距为10km,首台触发后3s、震后6s发布首次处理结果,与编目结果相比,震级偏差为-1.3,震中位置偏差为2.6km,盲区半径为18km。随着参与定位台站数量增多,震级与位置偏差越来越小,但震级仍整体偏小。河北地震预警网台站产出质量整体较高,其中烈度台作为地震预警最重要的组成部分,是决定预警效果的关键因素。本次地震震中距200km范围内,烈度台平均信噪比为48,震中距50km范围内平均信噪比为112,符合预警系统对信噪比的要求。本次地震预警结果表明,河北地震预警网内台站布局基本合理,波形质量较高,地震预警系统处理软件在本次地震中预警产出效果较好,已经具备了一定的地震预警能力。  相似文献   
39.
Yang  Wei  Zhang  Liping  Zhang  Yanjun  Li  Zongli  Xiao  Yi  Xia  Jun 《地理学报(英文版)》2019,29(3):389-405
Journal of Geographical Sciences - The Interconnected River System Network (IRSN) plays a crucial role in water resource allocation, water ecological restoration and water quality improvement. It...  相似文献   
40.
Liao  Xiaoyong  Tao  Huan  Gong  Xuegang  Li  You 《地理学报(英文版)》2019,29(10):1610-1624

A model integrating geo-information and self-organizing map (SOM) for exploring the database of soil environmental surveys was established. The dataset of 5 heavy metals (As, Cd, Cr, Hg, and Pb) was built by the regular grid sampling in Hechi, Guangxi Zhuang Autonomous Region in southern China. Auxiliary datasets were collected throughout the study area to help interpret the potential causes of pollution. The main findings are as follows: (1) Soil samples of 5 elements exhibited strong variation and high skewness. High pollution risk existed in the case study area, especially Hg and Cd. (2) As and Pb had a similar topo-logical distribution pattern, meaning they behaved similarly in the soil environment. Cr had behaviours in soil different from those of the other 4 elements. (3) From the U-matrix of SOM networks, 3 levels of SEQ were identified, and 11 high risk areas of soil heavy metal-contaminated were found throughout the study area, which were basically near rivers, factories, and ore zones. (4) The variations of contamination index (CI) followed the trend of construction land (1.353) > forestland (1.267) > cropland (1.175) > grassland (1.056), which suggest that decision makers should focus more on the problem of soil pollution surrounding industrial and mining enterprises and farmland.

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