吉林省伊通河生态需水量计算及分析

为研究吉林省伊通河生态需水量年内分配情况,采用流量历时曲线法分别对位于伊通河中下游的农安站及上游的伊通站的历史流量资料进行分析,得到伊通河农安站和伊通站的河道内最小生态需水量分别为4 693.66万 m³和303.52万 m³, 分别占年径流量的15.46%和4.80%;同时采用Tennant法和最小月流量平均法对伊通河最小生态需水量进行估算验证。Tennant法估算得到的最小生态需水量与流量历史曲线法较接近 。结果表明:在天然情况下,伊通河生态需水量的年内分配过程符合于实际水量分配过程,即多水时需水量多,少水时需水量少。农安段生态需水量可以有限地保护水生生物栖息地;而伊通段水生生物栖息地已经退化或贫瘠,需要采取措施对其进行保护治理。     

Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform

Urban water demand forecasting is crucial to reduce the waste of water resources and environmental protection. However, the non-stationarity and non-linearity of the water demand series under the influence of multivariate makes water demand prediction one of the long-standing challenges. This paper proposes a new hybrid forecasting model for urban water demand forecasting, which includes temporal convolution neural network (TCN), discrete wavelet transform (DWT) and random forest (RF). In order to improve the model’s forecasting abilities, the RF method is used to rank the factors and remove the less important factors. The dimension of raw data is reduced to improve calculating efficiency and accuracy. Then, the original water demand series is decomposed into different characteristic sub-series of multiple variables with better-behavior by DWT to weaken the fluctuation of original series. At the core of the proposed model, TCN is utilized to establish appropriate prediction models. Finally, to test and validate the proposed model, a real-world multivariate dataset from a water plant in Suzhou, China, is used for comparison experiments with the most recent state-of-the-art models. The results show that the mean absolute percentage error (MAPE) of the proposed model is 1.22% which is smaller than the other benchmark models. The proposed model indicates the only 2.2% of the prediction results have a relative error of more than 5%. It shows that the reliable results of the proposed model can be a superior tool for urban water demand forecasting.

     

Ensemble CNN Model for Effective Pipe Burst Detection in Water Distribution Systems

Various data-driven anomaly detection methods have been developed for identifying pipe burst events in water distribution systems (WDSs); however, their detection effectiveness varies based on network characteristics (e.g., size and topology) and the magnitude or location of bursts. This study proposes an ensemble convolutional neural network (CNN) model that employs several burst detection tools with different detection mechanisms. The model converts the detection results produced by six different statistical process control (SPC) methods into a single compromise indicator and derives reliable final detection decisions using a CNN. A total of thirty-six binary detection results (i.e., detected or not) for a single event were transformed into a six-by-six grayscale heatmap by considering multiple parameter combinations for each SPC method. Three different heatmap configuration layouts were considered for identifying the best layout that provides higher CNN classification accuracy. The proposed ensemble CNN pipe burst detection approach was applied to a network in Austin, TX and improved the detection probability approximately 2% higher than that of the best SPC method. Results presented in this paper indicate that the proposed ensemble model is more effective than traditional detection tools for WDS burst detection. These results suggest that the ensemble model can be effectively applied to many detection problems with primary binary results in WDSs and pipe burst events.

     

Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge

In the present study, an attempt is made to investigate and identify chaos using various techniques as well as river flow forecasting in short-term (daily) and mid-term (monthly) scales using nonlinear local approximation method (NLA) and ARIMA method. Daily and monthly flow data of Daintree River in Australia from 1969 to 2011 are used. In this respect, seven nonlinear dynamic methods including (1) average mutual information function; (2) phase space reconstruction; (3) false nearest neighbour algorithm; (4) method of surrogate data; (5) correlation dimension method; (6) Lyapunov exponent method; and (7) nonlinear local approximation are employed. The Takens’ theorem, mutual information and false nearest neighbour are used to determine the delay time and embedding dimension for the phase space reconstruction. The correlation dimensions obtained for the short term and mid-term river flow are 6.7 and 3.3, respectively. The finite dimensions obtained for the short term and mid-term river flow time series indicate the possible existence of chaos. The comparative analyses show that the NLA method is superior to ARIMA in mid-term scale while both models are acceptable for short term scale forecasting.     

城市给水系统优化调度研究

用水量预测研究和给水管网数学模型研究是优化调度的基础,用水鼍预测模型是在分析城市用水量序列数据模式的基础上,综合利用统计回归的方法建立的数学表达式;给水管网数学模型是建立水厂出厂压力和流量与管网测压点之间的经验数学表达式,它反映了给水系统的运行工况.优化调度模型的建立和求解是优化调度的核心.     

River Stage Forecasting using Enhanced Partial Correlation Graph

Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynamics of different water flow scenarios, which is not scalable for a large network of sensors. This paper presents a novel approach to model spatio-temporal dependencies across river network stations using a partial correlation graph. We also provide a method to enrich this partial correlation graph by eliminating the spurious correlations. We demonstrate the utility of enriched partial correlation graphs in multivariate forecasting for various scenarios and state-of-the-art multivariate forecasting models. We observe that the forecasting techniques that use information from the enriched partial correlation graph outperform standard time series forecasting approaches for river network forecasting.

     

Similarity-Based Optimal Operation of Water and Sediment in a Sediment-Laden Reservoir

Reservoir sedimentation is a severe problem because it leads to the loss of reservoir storage capacity. Therefore, the sustainable management of water and sediment constitutes a critical measure in reservoir operation. In this research, a model of similarity-based operation method of water and sediment which can improve the efficiency of reservoir operation strategies is presented. Two parts comprise this method: a similarity-based forecasting model for the sediment process at the dam, and a new operation mode of water and sediment based on the sediment process at the dam. The similarity-based forecasting model is built on the total flow model of water and sediment, which requires less data. Using the similarity among flood cases, the parameters of the model are calibrated by group, and are dynamically selected, an approach which enforces the suitability of parameters and improves the forecasting accuracy. In terms of the sediment process at the dam, the proposed optimal operation model of water and sediment improves the sediment-venting efficiency and saves the water resource.     

Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations

The transport and fate of admixtures at coastal zones are driven, or at least modulated, by currents. In particular, in tide-dominated areas due to higher near-bottom shear stress at strong currents, sediment concentration and turbidity are expected to be at maximum during spring tide, while algal growth rate likely is peaking up at slack currents during neap tide. Varying weather and atmospheric conditions might modulate the said dependencies, but the water quality pattern still is expected to follow the dominant tidal cycle. As tidal cycling could be predicted well ahead, there is a possibility to use water quality and hydrodynamic high-resolution data to learn past dependencies, and then use tidal hydrodynamic model for nowcasting and forecasting of selected water quality parameters.This paper develops data driven models for nowcasting and forecasting turbidity and chlorophyll-a using Artificial Neural Network (ANN) combined with Genetic Algorithm (GA). The use of GA aims to automate and enhance ANN designing process. The training of the ANN model is done by constructing input–output mapping, where hydrodynamic parameters act as an input for the network, while turbidity and chlorophyll-a are the corresponding outputs (desired target). Afterward, the prediction is carried out only by employing computed water surface elevation as an input for the trained ANN model. The proposed data driven model has successfully revealed complex relationships and utilized its experiential knowledge acquired from the training process for facilitating the subsequent use of the data driven model to yield an accurate prediction.     

Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements

The Nile River is considered the main life artery for so many African countries especially Egypt. Therefore, it is of the essence to preserve its water and utilize it very efficiently. Developing inflow-forecasting model is considered the technical way to effectively achieve such preservation. The hydrological system of the Nile River under consideration has several dams and barrages that are equipped with control gates. The improvement of these hydraulic structures’ criteria for operation can be assessed if reliable forecasts of inflows to the reservoir are available. Recently, the authors developed a forecasting model for the natural inflow at Aswan High Dam (AHD) based on Artificial Intelligence (AI). This model was developed based on the historical inflow data of the AHD and successfully provided accurate inflow forecasts with error less than 10%. However, having several forecasting models based on different types of data increase the level of confidences of the water resources planners and AHD operators. In this study, two forecasting model approach based on Radial Basis Function Neural Network (RBFNN) method for the natural inflow at AHD utilizing the stream flow data of the monitoring stations upstream the AHD is developed. Natural inflow data collected over the last 30 years at four monitoring stations upstream AHD were used to develop the model and examine its performance. Inclusive data analysis through examining cross-correlation sequences, water traveling time, and physical characteristics of the stream flow data have been developed to help reach the most suitable RBFNN model architecture. The Forecasting Error (FE) value of the error and the distribution of the error are the two statistical performance indices used to evaluate the model accuracy. In addition, comprehensive comparison analysis is carried out to evaluate the performance of the proposed model over those recently developed for forecasting the inflow at AHD. The results of the current study showed that the proposed model improved the forecasting accuracy by 50% for the low inflow season, while keep the forecasting accuracy in the same range for the high inflow season.     

Use of fluorescent in situ hybridisation for the visualisation of Helicobacter pylori in real drinking water biofilms.

A fluorescently labelled peptide nucleic acid (PNA) probe has been applied for the in situ detection of Helicobacter pylori in drinking water biofilms. The method was originally applied to real pipe samples removed from a drinking water distribution system (DWDS) but the curvature and the heavy fouling of the pipes prevented an accurate detection of the bacterium by epifluorescence microscopy. Therefore, two semi-circular flow cells were placed in a bypass of the DWDS, and coupons with up to 72 days of exposure were regularly sampled and analysed for the presence of H. pylori. In the flat surfaces of the coupons, it was possible to sparsely detect cells exhibiting similar morphology to H. pylori that were emitting the PNA probe fluorescent signal. Coupons were also visualised under the microscope before the hybridisation procedure to serve as negative controls and ensure the validity of the method. This work corroborates the findings already published elsewhere that this bacterium might be present in DWDS biofilms. The method requires, however, highly trained personnel for an accurate detection of the pathogen and will need simplification before being routinely used in standard water analysis laboratories.     

永定河官厅山峡河道内最小生态需水量的历时曲线法

随着永定河下游河道内径流的逐年减少,永定河生态需水研究逐渐成为一个迫切的问题。现针对永定河官厅山峡段河道内生态需水,采用流量历时曲线法分析其流量历时情况,计算得到永定河官厅水库(坝下)站和雁翅站的河道内最小生态需水量为1.06亿m3和1.98亿m3,分别占年径流量的11.3%和26.3%。同时采用Tennant法对计算结果进行比较验证,两种方法得到的结果比较相近。在此基础上,根据研究河段的历史流量历时情况对最小生态需水量进行年内分配计算,将最小生态需水量从年尺度降到月尺度,得到年内各月的最小生态需水量,为永定河流域的水资源优化配置研究提供参考。     

基于水文水动力学耦合的洪水预报模型研究及应用

为了提高下垫面变化剧烈流域的洪水预报精度,在传统流域水文模型的基础上耦合水动力学模型,建立水文水动力耦合洪水预报模型。首先利用水文模型获得某一断面的流量过程作为水动力学模型的边界条件;之后利用一维水动力学模型进行河道洪水演进计算,推求流域出口断面的流量过程;最后以烟台市外夹河流域为例进行验证。结果表明,所建水文水动力耦合模型模拟的产流合格率较高,流量过程与实测值吻合,在一定程度上弥补了集总式水文模型不能考虑河道内复杂水流运动的不足,因此对具有复杂水文、水力条件的流域的洪水预报具有重要的指导意义。     

基于VMD-BP模型的河流流量预测方法

河流流量是水文监测和水资源管理的重要指标,流量预测对于水利建设、航运规划和水资源调度等方面具有重要的指导意义和参考价值。结合变分模态分解(VMD)处理非平稳序列的优势以及BP神经网络(BPNN)处理非线性拟合的能力,提出和构建了基于VMD-BP模型的河流流量预测方法。以长江宜昌水文站为实例,基于1998年和1999年的日水位和日流量数据,对方法模型进行了验证。结果表明:VMD-BP模型在一定程度上解决了水位和流量的多值关系,降低了数据的波动性,预测结果优于线性拟合的回归模型和BPNN模型,预测误差仅为1.61%,为河流流量预测提供了一种有效的方法。     

基于BP神经网络的桂林生态城市建设需水量预测

根据桂林市经济社会历年统计的主要指标数据,运用SPSS社会科学统计软件分析并选取出桂林市辖区生态城市建设需水量的显著性影响因子,采用改进的归一化进行非线性规格化数据处理,基于Matlab建立BP神经网络模型,预测桂林市辖区生态城市建设需水量,结果表明,预测结果与原始数据的平均相对误差为1.19%,最大为2.08%,最小为0.28%。该模型具有较高的预测精度和良好的泛化能力,BP神经网络与SPSS软件优化组合模型,可用于需水量预测。     

基于联系隶属度的城市需水量预测模型

城市需水量预测是生态城市规划与管理的基础,但受诸多不确定因素影响,是一个复杂的预测难题。为能定量统一表达预测年份需水量各影响因素间及与历史数据间的交叉、交融的确定和不确定关系,在此应用有序聚类理论与集对分析的耦合方法,提出了基于联系隶属度概念的城市需水量预测模型。该模型首先基于城市需水量历史数据进行最优分割聚类,应用联系隶属度对预测年份需水量的影响因子与历史数据关系进行同异反分析,并构建相似模型预测相应年份的城市需水量。实例应用及与其他方法对比的结果表明,该模型应用于城市需水预测是有效可行的。     

基于RVA法的河流生态需水量研究

以河流生态水文变化的指标体系（Indicator of Hydrologic Alteration,IHA）为基础,立足整体河流水文情势的流量谱系,采用变异性范围法（Range of Variability Approach,RVA）,进行基于长系列历史流量资料的河流生态需水量估算,并以山西省5条较大河流的生态敏感断面为对象进行研究。结果表明,利用本方法估算的结果是合理的,可为河流生态需水量的估算和水资源的可持续开发利用提供科学依据。     

An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method

Daily inflow forecasts provide important decision support for the operations and management of reservoirs. Accurate and reliable forecasting plays an important role in the optimal management of water resources. Numerous studies have shown that decomposition integration models have good prediction capacity. Considering the nonlinearity and unsteady state of daily incoming flow data, a hybrid model of adaptive variational mode decomposition (VMD) and bidirectional long- and short-term memory (Bi-LSTM) based on energy entropy was developed for daily inflow forecast. The model was analyzed using the mean absolute error (MAE), the root means square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (r). A historical daily inflow series of the Baozhusi Hydropower Station, China, is investigated by the proposed VMD-BiLSTM with hybrid models. For comparison, BP, GRNN, ELMAN, SVR, LSTM, Bi-LSTM, EMD-LSTM, and VMD-LSTM, were adopted and analyzed for evaluation and analyzed. We found that the proposed model, with MAE?=?38.965, RMSE?=?64.783, and NSE?=?95.7%, was superior to the other models. Therefore, the hybrid model is robust and efficient for forecasting highly nonstationary and nonlinear streamflow. It can be used as the preferred data-driven tool to predict the daily inflow flow, which can ensure the safe operation of hydropower stations in reservoirs. As an interdisciplinary field spanning both machine learning and hydrology, daily inflow forecasting can become an important breakthrough in the application of deep learning to hydrology.

     

河道洪水流量预测方法研究

及时准确地预报某区域内河道指定区段洪水流量及发生时间,对合理实施该区域的防洪预案、落实抗洪抢险措施、组织调度人员及防汛物资具有重要意义。目前河道洪水预报普遍采用马斯京根流量演算法及加里宁—米尔加科夫洪水演进法,两种方法的参数率定存在局限性,对应支流的河道分段处理也存在问题。本文依据最小二乘法,建立含有预测河段上游干流、支流水文站（或水位站）流量或水位预测模型,该模型不受其他水文参数的率定精度影响,直接利用以往洪水及当次洪水上游、下游站的观测资料建立回归预测模型,并通过递推方式完成当次洪水预测,表达形式简单直观、便于实际应用。利用该模型完成了嫩江干流齐齐哈尔水文站2013年洪水流量预测,经与实测成果比较,洪峰流量最大拟合误差小于5.2%,具有较好的计算精度。     

Topological Robustness and Vulnerability Assessment of Water Distribution Networks

The reliability of a water distribution network (WDN) is a function of several time-invariant and time-dependent factors affecting its components and connectivity, most important of which have been shown to be the network’s topology, its operating pressure, the type of key components (such as the diameter, length, material and age of water pipes) and the network’s historical performance (such as the number of previously observed failures in the network). In terms of network topology, this attribute even though generally thought as time-invariant it actually is time-dependent, as the paths in a water distribution network change over time based on the hydraulics in the network (water demand and water pressure/flow alter the way water flows in the piping network). The work described herein examines the time-dependent nature of a WDN topology and by means of a betweenness centrality index (BC) method demonstrates the effect of topology on the network’s vulnerability / reliability. The importance of the betweenness centrality index is demonstrated by use of a case-study water distribution network operated under both normal and abnormal conditions. The proposed method is also coupled with spatial mapping to indicate areas of concern in the network, and with a decision support system to assist in prioritizing actions to improve on the network’s robustness and resilience.     

Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

In semi-arid regions, the deterioration in groundwater quality and drop in water level upshots the importance of water resource management for drinking and irrigation. Therefore geospatial techniques could be integrated with mathematical models for accurate spatiotemporal mapping of groundwater risk areas at the village level. In the present study, changes in water level, quality patterns, and future trends were analyzed using eight years (2012–2019) groundwater data for 171 villages of the Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps for the pre-monsoon season. These datasets were integrated with three different time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and Artificial Neural Network models for accurate prediction of groundwater level and quality parameters. Results reveal that the ANN model can describe groundwater level and quality parameters more accurately than the time series forecasting models. The change in groundwater level was observed with more than 4.0 m rise in 81 villages during 2012–2013, whereas ANN predicted results of 2023–2024 predict no rise in water level?>?4.0 m. However, based on predicted results of 2024, the water level will drop by more than 6.0 m in 16 villages of Phagi. Assessment of water quality index reveals unfit groundwater in 74% villages for human consumption in 2024. This time series and projected groundwater level and quality at the micro-level can assist decision-makers in sustainable groundwater management.