Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

基于前向神经网络的广义环境系统评价普适模型

为了建立由水环境、空气环境、生态环境、水资源环境、灾害环境、遥感环境、社会经济环境等不同环境系统组成的广义环境系统评价都能普适、通用的神经网络模型,针对BP神经网络因收敛速度慢、易于陷入局部极值而使实用性受限的缺陷,提出以双极性sigmoid函数作为网络隐层节点(神经元)的激活函数,而网络输出为所有隐层节点输出的线性求和的前向神经网络的广义环境系统评价模型.在设置广义环境系统指标参照值和指标值规范变换式,并对指标值进行规范变换的基础上,分别构建了适用于广义环境系统评价的任意2个指标规范值的前向神经网模型(NV-FNN(2)结构)和任意3个指标规范值的前向神经网模型(NV-FNN(3)结构).而对于指标较多的广义环境系统评价,只要将多指标分解为以上2个指标和3个指标的两种简单结构的前向神经网络的广义环境系统评价模型的组合表示即可.理论分析和实例检验结果表明:该模型对任意广义环境系统的规范指标值皆普适、通用,因而使不同环境系统的评价变得简洁、统一.规范变换和优化算法相结合的建模思想和方法对简化广义环境系统评价的多元回归、投影寻踪回归、回归支持向量机和径向基神经网络建模亦有借鉴和启迪作用.     

BP 神经网络模型在环境气象灾害预测中的应用研究--以湖南省为例

依据环境气象数据与自然灾害统计数据,建立BP神经网络模型,对湖南主要气象灾害（洪灾、旱灾、冰冻灾）及受灾经济损失进行实例预测,将在MATLAB7软件中的仿真结果与传统的多元线性回归模型分析结果进行比较和误差分析。结果表明,BP神经网络模型在洪灾、旱灾受灾率方面的预测效果和精度优于多元回归模型,而由于冰灾训练样本不足及经济损失与输入因子的线性相关程度高,在冰灾与受灾经济损失率方面稍逊于多元回归模型。     

Assessment of present and future risk to Italian forests and human health: Modelling and mapping

A review of ozone pollution in Italy shows levels largely above the thresholds established by EU regulation for vegetation and human health protection. The Italian air quality monitoring network appears quantitatively inadequate to cover all the territorial surface, because of scarcity and unequal distribution of monitoring sites. By applying the integrated assessment model RAINS-Italy to the year 2000, the whole of Italy exceeds the AOT40 critical level for forest, while Northern and central areas show strong potential of O₃ impact on human health with ∼11% of territory >10 O₃-induced premature deaths. Two scenarios for the year 2020, the Current Legislation and the Maximum Technical Feasible Reduction, show a reduction of AOT40Forest by 29% and 44%, SOMO35 by 31% and 47%, and O₃-induced premature deaths by 32% and 48%, compared to 2000. RAINS-Italy can be used to improve the map quality and cover areas not reached by the national monitoring network.     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

粒子群优化的RBF瓦斯涌出量预测

瓦斯涌出量是煤矿瓦斯灾害的主要来源,它直接影响煤矿安全生产和经济技术指标。瓦斯涌出量的传统预测方法是将其影响因素线性化后提出的,具有一定的局限性。本文基于群体智能理论,提出了一种基于粒子群算法优化的RBF神经网络瓦斯涌出量预测模型。研究表明RBF神经网络预测精度与网络权值和RBF参数初始值有很大关系,因此本文采用粒子群算法优化RBF网络权值和其他参数,形成PSO-RBF预测模型。该模型通过计算种群粒子的适应度,确定全局最优值,寻找网络参数的最优值。实验结果表明PSO-RBF优于传统的RBF预测模型,训练速度和预测精度显著提高。     

Elman神经网络在抽水地面沉降预测中的应用

将Elman神经网络应用于南京奥体中心地铁站抽水试验初期对地面沉降的预测，提出了一种预测模型。该模型采用对已知数据样本进行分段的方法，把分段后的数据作为网络输入和期望输出训练网络，从而达到动态建模的目的。从预测结果来看，该模型误差较小，基本上能够反映2002—07—21当天地面沉降的真实情况，具有一定的可靠性和实用性，因此，该模型可作为后期由于基坑开挖和防渗排水所引起地面沉降的分析工具。     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

平原河网地区滨岸带外来植物入侵现状及影响研究——以上海青浦区为例

河流滨岸带作为一种典型的开放系统,在受到外界干扰的条件下极易被外来植物所入侵并受到严重影响。以上海青浦区为例,通过对河流滨岸带生态系统入侵植物分布及现状调查,以及对两种入侵植物：白花三叶草（Trifolium repens）和加拿大一枝黄花（Solidago canadensis）群落的物种多样性及其土壤特性的实证研究,探讨外来植物入侵对河流滨岸带生态系统造成的影响。结果表明：目前该地区河流滨岸带外来入侵植物共有14科24种,其科、种数分别占到该生态系统所有植物的48．3％与26．1％。入侵植物白花三叶草与加拿大一枝黄花在春、秋两季均显著降低了河流滨岸带植物群落的物种多样性。此外,两种人侵植物也对当地河流滨岸及土壤特性造成了不同程度的影响,其中对土壤含水量、pH值的影响最为显著。该研究填补了平原河网地区河流滨岸带生态系统外来植物入侵研究的空白,有助于今后开展对该生态系统的评价、恢复及管理工作。     

珠三角工业城市污染物入河量空间特征分析

以东莞市第一次全国污染源普查成果为数据基础,调查估算2010年东莞市33个镇街和25个水功能区的COD、氨氮入河量,并应用GIS技术及相关分析法分析污染物入河量空间分布特征及其影响因素。研究发现：①东莞市各镇街及水功能区的污染物入河量呈明显不均匀分布,人口、GDP是影响污染物入河量空间分布的主要因素。②河网区、非河网区镇街的工业污染物入河量与GDP均呈较好的正相关关系,但非河网区单位污染物入河量GDP产出拟合曲线斜率是河网区的31倍,显示河网区镇街每增长单位污染物入河量所产生的GDP远远小于非河网区,河网区镇街往往付出巨大的污染代价而经济增长却非常有限。