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1.
河流水环境质量综合评价方法比较研究   总被引:12,自引:0,他引:12  
提出了对河流水环境质量综合评价方法的基本要求,对水环境质量评价方法的现状进行了研究,并通过对各种方法的 比较,探讨了今后水环境质量综合评价方法的发展方向。  相似文献   

2.
科学合理地布设水质监测断面是全面准确获取水环境质量监测数据的前提条件。利用研究区域现有断面2014年的监测数据,结合水资源调度方式和水环境功能区划要求,划分为五大片区并采用聚类分析方法进行断面优化,通过F检验和t检验表明优化前后断面无显著差异。同时,提出了一种综合的水环境质量考核办法——区域水环境综合评价考核指标,该指标包括骨干河道、乡村河道两类考核断面,采用综合水质评价与单因子评价相结合的方法,设置适当的权重系数,评价结果较为全面、客观地反映区域水环境质量状况,基本满足对区域水环境质量考核评价的管理需求。  相似文献   

3.
水环境生物监测是环境监测的重要内容,它应重点说清环境胁迫的生物效应。简述了总量管理、流域管理、风险管理、生态管理等环境管理对水环境生物监测有迫切需求,应引入"生态系统健康"、"生物完整性"、"环境胁迫"、"全排水毒性"等现代环境生物监测的基本概念,建立水环境生物监测技术发展的理论基础,发展生物完整性、综合毒性等监测与评价核心技术;革新现行监测方法体系,建立包括QA/QC、快速方法等支持系统在内的现代水环境生物监测业务化方法体系;创新评价技术体系,建立水环境生态健康评价及综合毒性评价指标体系、基准及分级管理标准,确立水环境质量管理的生物学目标。  相似文献   

4.
水质评价是目前水环境质量管理的重要支撑,水质综合评价方法已经逐渐由断面评价向流域综合评价进行转变与突破。该文对目前的断面水质评价方法进行了归类综述,并且对流域水质综合评价方法及其在各个国家流域的应用进行了重点评述。分析认为中国在流域评价中所使用的"断面比例法"不够准确,缺乏污染物时空分析以及生物评价等问题,并据此提出了未来的研究方向,为未来地表水质评价方法的提升以及水环境管理的完善提供科学的参考依据和建议。  相似文献   

5.
综合污染指数评价与水质类别判定的关系   总被引:20,自引:1,他引:20  
对水环境质量综合评价的定性、定量方法作了详细说明 ,并就综合污染指数评价方法与水质类别判定之间的相互关系、矛盾及总体的量化关系进行了分析 ;同时 ,对目前综合污染指数评价方法提出了改进意见。  相似文献   

6.
河流水质综合评价方法的统一和改进   总被引:42,自引:3,他引:42  
在分析我国河流水质综合评价方法存在的问题以及河流水质综合评价方法研究现状的基础上 ,以国家对全国水环境质量系统管理需要为目的 ,提出了对河流水质综合评价方法的要求 ,并针对综合污染指数法的缺陷 ,提出了改进的综合水质指数方法  相似文献   

7.
滇池水环境质量综合评价方法   总被引:1,自引:1,他引:0  
研究了2000—2017年滇池水环境质量时空变化和生态环境时空演替规律,结合滇池目前水环境质量监测工作要求,利用以往研究成果筛选出的36个评价指标,采用层次分析法,构建滇池水生态健康评价方法模型,得出滇池水生态健康评价标准,输入滇池湖体2011—2017年4个监测点位相关指标监测数据进行方法验证。验证结果表明,建立的评价方法能较为全面、准确、宏观地反映滇池水环境质量状况,与现有综合评价方法相比,能综合反映现有评价方法结果,且具有宏观、全面等优点,能很好地响应生物多样性变化,服务滇池的治理工作。  相似文献   

8.
基于改进的多目标决策的水环境质量综合评价   总被引:1,自引:0,他引:1  
在对水环境质量综合评价中,多目标决策-理想区间法解决了水环境质量评价标准是区间而非点的缺陷。但是在计算监测点到各理想区间向量的距离时,各水环境质量指标权重直接影响综合评价的结果,通常的确定方法是简单的假设各水环境质量指标的权重相等,这与实际情况相悖。为了解决这一问题,提出了将超标法用于多目标决策法中,利用超标法确定各水环境质量指标的权重,然后将其应用于多目标决策-理想区间法来分析水环境质量等级。并将改进后的多目标决策-理想区间法应用于珠江口及邻近海域的水环境中。基于超标法确定权重的多目标决策-理想区间法与聚类分析相比更有效,与等权重的多目标决策-理想区间法相比,更能体现水环境的污染状况,可应用于各种环境因子的综合评价中。  相似文献   

9.
利用灰色关联分析法对嘉峪关市地面水环境质量进行了评价,同时选用了模糊综合评价法来评价水质级别并进行比较,结果证明,两种方法评价结果一致,且灰色关联分析法评价嘉峪关地面水环境质量更符合实际状况。  相似文献   

10.
水环境质量评价是水资源可持续利用的重要前提,同时也是水环境管理与治理决策的重要依据。为科学评价黄河呼和浩特段水环境质量,基于基础资料数据库,遵循全面性、区域性、易量化性等原则,采用综合污染指数法详细分析了黄河呼和浩特段水环境质量的时空变化情况,进一步揭示了2020年水质季节变化规律,并对主要超标因子进行了深度解析。结果表明:随着一系列环境保护政策和管理措施的实施,2015—2020年黄河呼和浩特段水环境质量综合指数下降了75.87%。在时间尺度上,各断面的水环境质量综合指数平均值由2015年的93.81下降至2020年的22.64,整体水环境质量评价等级由G4转为G2,对应的水环境质量状况由中度污染转为较好;在空间尺度上,上游水环境质量优于下游,城区段水环境质量较差。此外,评价结果显示,部分水体当前仍存在氨氮超标问题。评价结果较为客观地反映了黄河呼和浩特段的水环境质量,可为水环境管理提供决策依据。  相似文献   

11.
Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.  相似文献   

12.
按照环境监测网络的信息处理结构,将网络划分为采集层、网络层和应用层,归纳了数据级融合、特征级融合、决策级融合3层融合级别,介绍了加权平均、卡尔曼滤波、人工神经网络、支持向量机、遗传算法、贝叶斯系统等在环境监测数据分析中的应用,指出当前面临的数据处理技术不成熟、评价体系不完善和数据质量监管能力不足等问题,提出优化网络结构、数据多元应用和强化决策支持等研究建议。  相似文献   

13.
Soil water content is a key parameter for representing water dynamics in soils. Its prediction is fundamental for different practical applications, such as identifying shallow landslides triggering. Support vector machine (SVM) is a machine learning technique, which can be used to predict the temporal trend of a quantity since training from past data. SVM was applied to a test slope of Oltrepò Pavese (northern Italy), where meteorological parameters coupled with soil water content at different depths (0.2, 0.4, 0.6, 1.0, 1.2, 1.4 m) were measured. Two SVM models were developed for water content assessment: (i) model 1, considering rainfall amount, air temperature, air humidity, net solar radiation, and wind speed; (ii) model 2, considering the same predictors of model 1 together with antecedent condition parameters (cumulated rainfall of 7, 30, and 60 days; mean air temperature of 7, 30, and 60 days). SVM model 2 showed significantly higher satisfactory results than model 1, for both training and test phases and for all the considered soil levels. SVM models trends were implemented in a methodology of slope safety factor assessment. For a real event occurred in the tested slope, the triggering time was correctly predicted using data estimated by SVM model based on antecedent meteorological conditions. This confirms the necessity of including these predictors for building a SVM technique able to estimate correctly soil moisture dynamics in time. The results of this paper show a promising potential application of the SVM methodologies for modeling soil moisture required in slope stability analysis.  相似文献   

14.
Magnetic solid-phase extraction based on coated nano-magnets Fe3O4 was applied for the preconcentration of four polycyclic aromatic hydrocarbons (PAHs; anthracene, phenanthrene, fluorine, and pyrene) in environmental water samples prior to simultaneous spectrophotometric determination using multivariate calibration method. Magnetic nanoparticles, carrying target metals, were easily separated from the aqueous solution by applying an external magnetic field so, no filtration or centrifugation was necessary. After elution of the adsorbed PAHs, the concentration of PAHs was determined spectrophotometrically with the aid of a new and efficient multivariate spectral analysis base on principal component analysis-projection pursuit regression, without separation of analytes. The obtained results revealed that using projection pursuit regression as a flexible modeling approach improves the predictive quality of the developed models compared with partial least squares and least squares support vector machine methods. The method was used to determine four PAHs in environmental water samples.  相似文献   

15.
The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.  相似文献   

16.
Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making  相似文献   

17.
Wang  Jing  Geng  Yan  Zhao  Qiuna  Zhang  Yin  Miao  Yongtai  Yuan  Xumei  Jin  Yuxi  Zhang  Wen 《Environmental Modeling and Assessment》2021,26(4):529-541

With the increasingly serious problem of surface water environmental safety, it is of great significance to study the changing trend of reservoir water quality, and it is necessary to establish a water quality prediction and early warning system for the management and maintenance of water resources. Aiming at the problem of water quality prediction in reservoirs, a CA-NARX algorithm is designed, which combines the improved dynamic clustering algorithm with the idea of machine learning and the forward dynamic regression neural network. The improved dynamic clustering algorithm is used to classify the eutrophication degree of waterbodies according to the total phosphorus and total nitrogen content. Considering four meteorological factors, air temperature, water temperature, water surface evaporation, and rainfall, synthetically for each water quality condition, the total phosphorus and total nitrogen in the waterbody are forecasted by an improved forward NARX dynamic regression neural network. Based on this, the CA-NARX prediction algorithm can realize short period water quality prediction. Compared with the traditional support vector regression machine model, improved GA-BP neural network, and exponential smoothing method, the CA-NARX model has the least prediction error.

  相似文献   

18.
This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH3) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ 2. These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH3 concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH3 concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH3 concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH3 concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH3 concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH3 dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH3 sensor discrete data sources.  相似文献   

19.
To address current challenges regarding sustainable development and support planning for this form of development, new learning about different possible futures and their potential sustainability implications is needed. One way of facilitating this learning is by combining the futures studies and sustainability assessment (SA) research fields.This paper presents the sustainability assessment framework for scenarios (SAFS), a method developed for assessing the environmental and social risks and opportunities of future scenarios, provides guidelines for its application and demonstrates how the framework can be applied. SAFS suggests assessing environmental and social aspects using a consumption perspective and a life cycle approach, and provides qualitative results. SAFS does not suggest any modelling using precise data, but instead offers guidelines on how to carry out a qualitative assessment, where both the process of assessing and the outcome of the assessment are valuable and can be used as a basis for discussion.The benefits, drawbacks and potential challenges of applying SAFS are also discussed in the paper. SAFS uses systems thinking looking at future societies as a whole, considering both environmental and social consequences. This encourages researchers and decision-makers to consider the whole picture, and not just individual elements, when considering different futures.  相似文献   

20.
基于集合经验模态分解和支持向量机的溶解氧预测   总被引:1,自引:0,他引:1  
应用集合经验模态分解(EEMD)和支持向量机(SVM)相结合的方法,建立一种天然水体溶解氧浓度预测模型。首先,利用EEMD方法将溶解氧时序分解成不同频段的分量,以降低序列的非平稳性;然后,根据各序列分量的自身特征建立合适的SVM预测模型,此过程通过相关分析确定各分量输入量;最后,将各子分量预测值合成得到最终的预测结果。使用该模型对嘉陵江北温泉段的溶解氧浓度进行预测,结果表明,与传统单一的SVM和BP神经网络模型相比,该模型能有效提高预测精密度,具有良好的应用前景。  相似文献   

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