Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

Optimization of Shanghai Marine Environmental Monitoring Sites in the Identification of Boundaries of Different Water Quality Grades

Water quality is critical to ensure that marine resources and the environment are utilized in a sustainable manner. The objective of this study is therefore to investigate the optimum placement of marine environmental monitoring sites to monitor water quality in Shanghai, China. To improve the mapping or estimation accuracy of the areas with different water quality grades, the monitoring sites were fixed in transition bands between areas of different grades rather than in other positions. Following bidirectional optimization method, first, 18 candidate sites were selected by filtering out specific site categories. Second, three of these were, in turn, eliminated because of the rule defined by the changes in the areas of water quality grades and by the standard deviation of the interpolation errors of dissolved inorganic nitrogen(DIN) and phosphate(PO_4-P). Furthermore, indicator kriging was employed to depict the transition bands between different water quality grades whenever new sampling sites were added. The four optimization projects of the newly added sites reveal that, all optimized sites were distributed in the transition bands of different water grades, and at the same time in the areas where the historical sites were sparsely distributed. New sites were also found in the overlap region of different transition bands. Additional sites were especially required in these regions to discriminate the boundaries of different water quality grades. Using the bidirectional optimization method of the monitoring sites, the boundaries of different water quality grades could be determined with a higher precision. As a result, the interpolation errors of DIN and PO_4-P could theoretically decrease.

Investigation on Phytoplankton and Water Quality Evaluation in Landscape Water of Campus in Nanjing Xianlin University Town

The major phytoplankton was investigated and analyzed in landscape water of six campuses in Nanjing Xianlin University Town,and water quality was evaluated by single factor assessment method and comprehensive weighted evaluation method.The result showed that the major phytoplankton groups were Cyanophyta,Chlorophyta and Bacillariophyta.Besides,each evaluation indicator showed that waterbodies in four campuses were eutrophicated and result of single factor evaluation showed water quality all belonged to poor category V.The result of comprehensive weighted assessment showed that waters in Nanjing Normal University and Nanjing University of Posts and Telecommunications were seriously polluted,cyanobacterial bloom appearing.Waters in Nanjing University of Chinese Medicine and Nanjing Forest Police College hadn't been eutrophicated.

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.

Impact of Untreated Sedimentation Tank Sludge Water Recycle on Water Quality During Treatment of Low Turbidity Water

The overall purpose of this research is to examine the impact of untreated sedimentation tank sludge water( USTSW) recycle on water quality during treatment of low turbidity water in coagulation—sedimentation processes. 950 m L of raw water and different concentrations of 50 m L USTSW are injected into six 1 000 m L beakers without coagulant.The results indicate that USTSW characterized as accumulated suspended solids and organic matter has active ingredients,which possess the equivalent function of coagulant. The optimal blended water turbidity is in the range of 10-20 NTU,within which USTSW recycle achieves the highest save coagulant rate. The mechanism of strengthening coagulation effect when USTSW recycle mainly depends on the chemical effect and physical effect. What is more,through scanning electron microscopy( SEM),it is found that the floc structures with USTSW recycle are more compact than those without USTSW recycle. Besides,the water quality parameters of color,NH3-N,CODMn,UV254,total aluminum,total manganese when USTSW recycle is better than the raw water without recycle,indicating that USTSW recycle can improve water quality with strengthening coagulation effect.

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

Aquaculture has long been a critical economic sector in Taiwan.Since a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture water is essential.This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality.Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality.Since aquaculture water quality depends on a continuous interaction among multiple factors,and the current state is correlated with the previous state,a model with time series is required.Therefore,this study used recurrent neural networks(RNNs)with sequential characteristics.Commonly used RNNs such as long short-term memory model and gated recurrent unit(GRU)model have a memory function that appropriately retains previous results for use in processing current results.To construct a suitable RNN model,this study used Taguchi method to optimize hyperparameters(including hidden layer neuron count,iteration count,batch size,learning rate,and dropout ratio).Additionally,optimization performance was also compared between 5-layer and 7-layer network architectures.The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study.The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%,root mean square error of 0.0638,and R-value of 0.9984.Therefore,thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data,which is essential for a timely response to water quality issues.This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company.Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.

基于B/S和SOA架构的LIMS系统设计与研究

为提升水质检测业务的专业化、智能化、系统化、自动化水平,针对新发展阶段下水利高质量发展对水质信息管理的需求,研究并开发基于B/S系统架构和SOA设计架构的新型综合性水质信息管理系统(LIMS),运用仪器谱图识别及数据提取、PHP与MySQL融合、仪器谱图与原始记录自动关联等技术,在一定程度上提高水质数据的电子化无纸化获取、处理、传输及应用等方面的管理水平,实现水质检测业务流程与资源的动态关联和闭环追溯,提升水质监测评价工作的管理效率和质量管理能力。基于B/S和SOA架构的LIMS在前沿高新技术中的应用研究,为水质检测和管理的各项活动提供智能、高效的数据支持平台,也为水利行业信息系统管理建设及水域功能监测监管提供技术支撑。

经验小波变换和改进S变换结合的电能质量检测与识别方法

为分析不确定干扰因素影响下的实际电力网络电能质量问题,提出一种经验小波变换(EWT)和改进S变换相结合的电能质量检测与识别方法。该方法一方面利用EWT联合归一化直接正交(NDQ)算法和奇异值分解(SVD)算法准确提取调幅-调频分量的频率、幅值和时间参数,另一方面考虑到EWT算法在高噪声环境下瞬时幅值波动的问题,引入改进S变换提取高噪声干扰下的电能质量扰动时频信息,最后,基于EWT和改进S变换提取的扰动特征向量,利用基于改进粒子群优化算法(IPSO)优化支持向量机(SVM)的电能质量扰动识别分类器实现扰动类型的精确识别。仿真和实验表明所提方法在复合扰动识别分类时平均识别准确率为93.23%,且能够准确识别4种实测扰动信号。

Study on Sustainable Rainwater Resource Utilization—Towards Deepening of Homo Environmentics

This paper mainly investigated the value of the rainwater by introducing a “Logic of Encounter” that is a new logic beyond the logos and lemma through the metaphors which compare the real rainwater to one’s life. A consideration regarding sustainable rainwater resource utilization has been described and the main results are summarized in the paper.

Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity

The water quality grades of phosphate（PO4-P） and dissolved inorganic nitrogen（DIN） are integrated by spatial partitioning to fit the global and local semi-variograms of these nutrients. Leave-one-out cross validation is used to determine the statistical inference method. To minimize absolute average errors and error mean squares,stratified Kriging（SK） interpolation is applied to DIN and ordinary Kriging（OK） interpolation is applied to PO4-P.Ten percent of the sites is adjusted by considering their impact on the change in deviations in DIN and PO4-P interpolation and the resultant effect on areas with different water quality grades. Thus, seven redundant historical sites are removed. Seven historical sites are distributed in areas with water quality poorer than Grade IV at the north and south branches of the Changjiang（Yangtze River） Estuary and at the coastal region north of the Hangzhou Bay. Numerous sites are installed in these regions. The contents of various elements in the waters are not remarkably changed, and the waters are mixed well. Seven sites that have been optimized and removed are set to water with quality Grades III and IV. Optimization and adjustment of unrestricted areas show that the optimized and adjusted sites are mainly distributed in regions where the water quality grade undergoes transition.Therefore, key sites for adjustment and optimization are located at the boundaries of areas with different water quality grades and seawater.

基于改进NSGA-Ⅲ算法的供水管网多目标优化设计

针对多目标优化算法在供水管网优化设计中求解效率低、收敛性不佳等问题,提出一种自适应第三代非支配排序遗传算法(a-NSGA-Ⅲ),差别化分配交叉变异概率的同时对解集的多样性进行优化,统筹考虑供水管网的经济性、可靠性、水质情况,并在可靠性中引入节点富余水头绝对值均值与均方差以防止负压出现,提高压力及分布均匀性,最后以苏州某市供水管网为例,采用标准NSGA-Ⅲ和a-NSGA-Ⅲ算法对模型求解。结果表明,a-NSGA-Ⅲ算法在帕累托(Pareto)解集前沿分布、收敛性、方案结果上均优于标准NSGA-Ⅲ算法,在实际供水管网模型中效果更好,可为供水管网的设计与优化提供理论支撑。

Study of the Physico-Chemical and Bacteriological Characteristics of Drinking Water in So-Ava,South Benin

The present study assesses the physicochemical and bacteriological quality of the drinking water used by the population of S?-Ava based on the Beninese standards and those established by the World Health Organization (WHO). In rural and peri-urban areas of Benin where public water supply systems are inadequate or almost non-existent, the population consumes water of various sources of unknown qualities. A total of 67 water samples were analyzed during the rainy season (July 2017) and in the dry season (January 2018) for certain physical, chemical and bacteriological parameters using the standard methods. The results of the analyses reveal that the physicochemical characteristics of the water used for consumption in S?-Ava comply with the drinking water standards of the World Health Organization and those in force in Benin except for the percentages of the following parameters: pH (41.80%);turbidity (25.37%);the color (16.42);ammonium (17.91%);iron (40.30%);Nitrites (4.48%);Residual chlorine (91.05). Bacteriologically, the analyses showed a high total aerobic mesophilic flora contamination, faecal coliforms, E. coli, faecal enterococci respectively in 89.55%, 82.09%, 50.75% and 70.15% of the analyzed water samples. The ratio of faecal coliforms to faecal enterococci indicated that the origin of faecal contamination was human in 59.7% of the samples and animal in 40.3% of the samples. The adoption of hygiene measures at the water point, during the transport and storage of water, including the treatment by chlorination of drinking water at the family level was recommended for the population concerned and household awareness on the adoption of basic hygiene and sanitation measures have been recommended for hygiene and sanitation services.

PROGRESS IN RESEARCH ON RIVER WATER CHEMISTRY IN CHINA

River water resource is the most important component of water resources in China. This paper reviews the progress in the research on river water chemistry in China. It includes three parts: 1) the development of river water quality monitoring in China (at present, there exist three water quality monitoring networks in China: near 3000 water quality monitoring stations under the Ministry of Water Resources, several thousands water quality monitoring sites under the State Environmental Protection Administration and four sites under the China’s GEMS/Water Program); 2) progress in the research on chemical characteristics of river water chemistry in China and their geographical roles on natio wide and region wide scales; and 3) progress in the research on river quality changes in the last 40 years (the long term monitoring data reveals that the water quality of the Changjiang River has acidification trend, the Songhuajiang River had alkalization trend, and the Huanghe River has concentration trend in the last 4 decades).

Research and Application of Pollution Control in the Middle Reach of Ashe River by Multi-Objective Optimization

Based on one-dimensional water quality model and nonlinear programming, the point source pollution reduction model with multi-objective optimization has been established. To achieve cost effective and best water quality, for us to optimize the process, we set pollutant concentration and total amount control as constraints and put forward the optimal pollution reduction control strategy by simulating and optimizing water quality monitoring data from the target section. Integrated with scenario analysis, COD and ammonia nitrogen pollution optimization wasstudiedin objective function area from Mountain Maan of Acheng to Fuerjia Bridge along Ashe River. The results showed that COD and NH3-N contribution has been greatly reduced to AsheRiverby 49.6% and 32.7% respectively. Therefore, multi-objective optimization by nonlinear programming for water pollution control can make source sewage optimization fairly and reasonably, and the optimal strategies of pollution emission are presented.

A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

Neural networks(NNs)have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation.An essential step in developing an NN is the hyperparameter selection.In practice,it is common to manually determine hyperparameters in the studies of NNs in water resources tasks.This may result in considerable randomness and require significant computation time;therefore,hyperparameter optimization(HPO)is essential.This study adopted five representatives of the HPO techniques in the surface water quality prediction tasks,including the grid sampling(GS),random search(RS),genetic algorithm(GA),Bayesian optimization(BO)based on the Gaussian process(GP),and the tree Parzen estimator(TPE).For the evaluation of these techniques,this study proposed a method:first,the optimal hyperparameter value sets achieved by GS were regarded as the benchmark;then,the other HPO techniques were evaluated and compared with the benchmark in convergence,optimization orientation,and consistency of the optimized values.The results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence,reasonable optimization orientation,and the highest consistency rates with the benchmark values.The optimization consistency rates via TPE for the hyperparameters hidden layers,hidden dimension,learning rate,and batch size were 86.7%,73.3%,73.3%,and 80.0%,respectively.Unlike the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test,the proposed benchmark-based HPO evaluation approach is feasible and robust.

Indoor Environment Quality Monitoring and Evaluation System Based on LoRa Communication

Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.

基于特征工程和NGO-LSTM的水质预测模型研究

由于水质数据特征复杂、关联度参差不齐而导致溶解氧浓度预测难度较大,为提高水质溶解氧浓度预测的准确性,提出了一种基于特征工程和北方苍鹰优化算法的长短期记忆网络(Feature Engineering-Northern Goshawk Optimization-Long Short Term Memory,FE-NGO-LSTM)混合模型。首先对水质数据集进行缺失值补齐、特征筛选与特征多项式构造,然后基于NGO-LSTM模型优化模型参数,提升预测性能;对不同多项式阶数下的特征预测效果进行分析之后,将该模型与基于灰狼优化算法、鲸鱼优化算法及粒子群优化算法的LSTM模型进行对比;最后,在太湖流域东苕溪城南监测断面对该模型进行了验证,计算FE-NGO-LSTM模型预见期为4,8,12,16,20,24 h的预测结果。试验结果显示:当多项式阶数为2阶时,模型预测效果最好,FE-NGO-LSTM模型相比基于其他优化算法的LSTM模型,平均绝对误差、均方误差、均方根误差分别至少降低9.0%,12.9%及6.3%,且随着预见期的增加,预测误差仍在可接受范围内,说明FE-NGO-LSTM模型在预测溶解氧浓度时具有一定优势与泛化性。

基于IGWO算法优化LSSVM的电能质量扰动识别方法

为了提高电能质量扰动(power quality disturbance,PQD)识别结果的准确性,笔者提出一种基于改进灰狼优化算法(improved grey wolf optimization,IGWO)优化最小二乘支持向量机(least squares support vector machine,LSSVM)的PQD识别方法。通过采用收敛因数指数调整、自适应位移和权重动态修订等措施对灰狼优化算法进行改进,得到IGWO算法;以PQD信号的9个特征量为支持向量、7种PQD类型为输出量,利用IGWO算法寻找LSSVM的最优参数,建立基于IGWO-LSSVM的PQD识别模型并进行仿真分析,且与其他模型的识别结果进行对比。结果表明,相比算例中列出的几种对比模型,IGWO-LSSVM模型识别结果的正确率更高,验证了所提PQD识别方法的有效性和实用性。

基于改进S变换的电能质量扰动信号特征提取方法

鉴于S变换时频分辨率低、计算量大,实际应用受限,该文构建改进S变换算法,提出基于改进S变换的电能质量扰动信号特征提取方法。首先计算采样信号的快速傅里叶变换(fast Fourier transform,FFT)频谱,创立基于包络极值算法的特征频率保留机制与信号无关频率点的自动识别机制,剔除非特征信息;然后依据信号频率与频段分析需求,引入新的窗宽调节尺度因子构成Gauss自适应优化窗,克服传统Gauss窗主瓣较宽、频率分辨率低的局限,构建基于Gauss自适应优化窗的改进S变换;对特征频率点进行变换处理,提取特征向量,实现扰动信号的自适应检测。仿真分析和试验结果表明,本文提出的改进S变换算法提高了电能质量扰动信号的时频分析准确率,计算量小,适用于电力系统扰动信号的准确、快速检测。