基于BP神经网络模型在珊溪水库水华预测中的应用

在珊溪水库藻类暴发期间应急监测数据的基础上,建立pH值、高锰酸盐指数、总氮、总磷、叶绿素a数据矩阵。运用MATLAB R2015b GUI可视化界面模块,将应急监测数据样本空间分为训练样本、验证样本、测试样本,建立珊溪水库BP神经网络模型,预测了珊溪水库藻类暴发期间叶绿素a浓度。BP神经网络建模结果显示:输出数据与实测数据相关系数0.978,平均相对误差-0.19%,标准方差18.54%,模型稳定性较好,叶绿素a预测结果符合预期。BP神经网络预测模型为珊溪水库饮用水水源地环境保护提供了科学依据。     

Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network

A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.     

Assessing the Effects of Nutrient Management in an Estuary Experiencing Climatic Change: The Neuse River Estuary, North Carolina

Eutrophication is a serious water quality problem in estuaries receiving increasing anthropogenic nutrient loads. Managers undertaking nutrient-reduction strategies aimed at controlling estuarine eutrophication are faced with the challenge that upstream freshwater segments often are phosphorus (P)-limited, whereas more saline downstream segments are nitrogen (N)-limited. Management also must consider climatic (hydrologic) variability, which affects nutrient delivery and processing. The interactive effects of selective nutrient input reductions and climatic perturbations were examined in the Neuse River Estuary (NRE), North Carolina, a shallow estuary with more than a 30-year history of accelerated nutrient loading and water quality decline. The NRE also has experienced a recent increase in Atlantic hurricanes and record flooding, which has affected hydrology and nutrient loadings. The authors examined the water quality consequences of selective nutrient (P but not N) reductions in the 1980s, followed by N reductions in the 1990s and an increase in hurricane frequency since the mid-1990s. Selective P reductions decreased upstream phytoplankton blooms, but increased downstream phytoplankton biomass. Storms modified these trends. In particular, upstream annual N and P concentrations have decreased during the elevated hurricane period. Increased flushing and scouring from storms and flooding appear to have enhanced nutrient retention capabilities of the NRE watershed. From a management perspective, one cannot rely on largely unpredictable changes in storm frequency and intensity to negate anthropogenic nutrient enrichment and eutrophication. To control eutrophication along the hydrologically variable freshwater–marine continuum, N and P reductions should be applied adaptively to reflect point-source–dominated drought and non–point-source–dominated flood conditions.     

Watershed scale assessment of nitrogen and phosphorus loadings in the Indian River Lagoon basin,Florida

There is a growing evidence that the ecological and biological integrity of the lagoon has declined during the last 50 years, probably due to the decline in water quality. Establishment of a watershed scale seagrass-based nutrient load assessment is the major aim of water quality management in the Indian River Lagoon (IRL). Best estimate loadings incorporate wet and dry deposition, surface water, groundwater, sediment nutrient flux, and point source effluent discharge data. On the average, the IRL is receiving annual external loadings of 832, 645 and 94,476kg of total nitrogen (TN) and total phosphorus (TP), respectively, from stormwater discharges and agricultural runoff. The average internal cycling of TN and TP from sediment deposits in the IRL was about 42,640kg TN and 1050kg TPyr(-1). Indirect evidence suggests that atmospheric deposition has played a role in the ongoing nutrient enrichment in the IRL. The estimated total atmospheric deposition of TN and TP was about 32,940 and 824kgyr(-1), while groundwater contribution was about 84,920 and 24,275kgyr(-1), respectively, to the surface waters of the IRL. The estimated annual contribution of point effluent discharge was about 60,408kg TN and 7248kg TP. In total, the IRL basin is receiving an annual loading of about 1,053,553kg TN and 127,873kg TP. With these results, it is clear that the current rate of nutrient loadings is causing a shift in the primary producers of the IRL from macrophyte to phytoplankton- or algal-based system. The goal is to reverse that shift, to attain and maintain a macrophyte-based estuarine system in the IRL.     

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.     

Analysis of Lower Green Bay and Fox River,Collingwood Harbour,Spanish Harbour,and the Metro Toronto and Region remedial action plan (RAP) processes

This article presents a model of remedial action planning, which includes four key variables that determine progress in plan development and implementation and explain the differing level of achievement in individual sites. The model is illustrated by the characteristics and developments of four remedial action plan (RAP) processes (Lower Green Bay and Fox River, Collingwood Harbour, Spanish Harbour, and the Metro Toronto and Region RAPs). Differences in the local context of the plans have, to a significant degree, predisposed individual planning and implementation experiences. Local context includes three variables, namely geographical—technical and sociopolitical aspects and the previous history of water pollution management in the area. RAP precursors are a necessary precondition for progress in planning and substantive achievements. While there is a tendency that most geographically focused RAPs in administratively simple areas accomplish most, the motivation and political clout of RAP participants are strongly intervening factors. Resource input from upper levels of government, in particular financial commitment for plan implementation, is the fourth necessary ingredient for progress due to the RAPs' weak regulatory and institutional framework. Unfortunately, upper levels of government have shown widespread reluctance to lead in remedial action planning. This was only in part offset by local commitment and support for RAP and its cause.     

Predicting Particulate Matter (PM2.5) Concentrations in the Air of Shahr‐e Ray City,Iran, by Using an Artificial Neural Network

Particulate matter (PM), along with other air pollutants, pose serious hazards to human health. The Artificial Neural Network (ANN) is a branch of artificial intelligence that has an ability to make accurate predictions. In this article, the authors describe such methods and how historical data on air quality, moisture, wind velocity, and temperature in Shahr‐e Ray City, located at the southern tip of Tehran, was used to train an ANN to provide accurate predictions of PM concentrations. The availability of such predictions can offer significant assistance to those who are working to reduce air pollution.     

Local IWRM organizations for groundwater regulation: The experiences of the Aquifer Management Councils (COTAS) in Guanajuato, Mexico

Evidence of groundwater management by aquifer users emerging under Integrated Water Resources Management (IWRM) initiatives is presented, by analyzing the Consejos Técnicos de Aguas (COTAS; Technical Water Councils or Aquifer Management Councils) in the state of Guanajuato, Mexico, established between 1998 and 2000 by the Guanajuato State Water Commission (CEAG). Two contrasting models influenced this attempt to promote user self-regulation of groundwater extractions: locally autonomous aquifer organizations with powers to regulate groundwater extractions versus aquifer organizations with advisory powers only. The COTAS were conceived as locally autonomous IWRM organizations consisting of all aquifer users that would work together to reduce groundwater over-extraction and stabilize aquifer levels, at a later stage. CEAG followed an expedient IWRM approach to develop the COTAS, setting achievable targets for their development and explicitly focusing on active stakeholder participation. The article shows that, due to struggles between the state and federal levels, the COTAS have become advisory bodies that have not led to reductions in groundwater extractions. It concludes that achieving user self-regulation of groundwater extractions requires a fuller delegation of responsibilities to the COTAS which would not be possible without addressing the institutional struggles over water governance at the state and federal levels.