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.     

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。     

基于BP神经网络的鄱阳湖水位模拟

考虑到鄱阳湖水位受流域五河与长江来水等多因素的共同作用而表现出高度非线性响应,采用典型的三层BPNN神经网络模型来模拟鄱阳湖水位与其主控因子之间的响应关系。分别将湖口、星子、都昌、棠荫和康山水位作为目标变量进行BPNN模型构建和适用性评估。结果显示：综合考虑流域五河及长江来水（汉口或九江）的BPNN水位模型,空间站点水位模拟精度（R2和Ens）可达090以上,各站点的均方根误差（RMSE）变化范围约050～10 m,若忽略长江来水的影响作用,仅将流域五河来水作为湖泊水位的主控影响因子,模型训练期与测试期的纳希效率系数（Ens）和确定性系数（R2）显著降低,且低于050,均方根误差（RMSE）也明显增大（124～288 m）,意味着综合考虑流域五河与长江来水是获取结构合理、精度保证的鄱阳湖水位模型的重要前提。同时建议针对鄱阳湖湖盆变化对水位的影响,尽可能选择一致性较好的长序列数据集来训练和测试BPNN模型。所构建的BPNN神经网络模型可进一步结合流域水文模型,用来预测气候变化与人类活动下流域径流变化对湖泊水位的潜在影响,也可作为一种有效的模型工具来回答当前鄱阳湖一些备受关注的热点问题,如定量区分流域五河与长江来水对湖泊洪枯水位的贡献分量,为湖泊洪涝灾害的防治和对策制定提供科学依据     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

基于BP神经网络的矿车运行时矿井摩擦阻力的预测*

为了快速有效地确定矿车等运输设备在巷道内运行时矿井摩擦阻力的变化情况,克服模拟软件计算量和现场实测工作量大的问题,以巷道风流速度、矿车运行速度、阻塞比、矿车长度4个矿车运行时巷道摩擦阻力的影响因素作为切入点,采用动网格技术模拟得到矿车在巷道内运行时有关矿井摩擦阻力的数据,以此为样本构建基于BP神经网络的矿井摩擦阻力预测模型,运用MATLAB软件进行网络训练,并将BP神经网络预测值与FLUENT模拟值进行对比。研究结果表明:BP神经网络结构比较简单,能以较快速度收敛,预测值与模拟值最大误差在7%以内,该神经网络模型用于求解矿车等运输设备在巷道内运行时摩擦阻力的变化情况是可行的。     

Sources of organisational resiliency during the Thailand floods of 2011: a test of the bonding and bridging hypotheses

Based on the Institutional Collective Action framework, this research tests the impact of two competing hypotheses—bonding and bridging—on enhancing organisational resiliency. The bonding hypothesis posits that organisational resiliency can be achieved if an organisation works closely with others, whereas the bridging hypothesis argues that such a structure places considerable stress on an organisation and advocates for an organisation to position itself as a central actor to gain access to novel resources from a diverse set of entities to achieve resiliency. The paper analyses data gathered from semi‐structured interviews with 44 public, private, and non‐profit organisations serving communities affected by the Great Floods of 2011 in the Thai capital, Bangkok (urban), and in Pathum Thani (suburban) and Ayutthaya (rural) provinces. The findings suggest that: organisational resiliency was associated with the bridging effect; organisations in the rural province were more resilient than those in the suburban and urban centres; and private and non‐governmental organisations generally were more resilient than public sector organisations. The findings highlight the importance of fostering multi‐sector partnerships to enhance organisational resiliency for disaster response.     

贫困退出背景下返贫脆弱性评价——融合区域与个体的新视角

精准识别返贫脆弱性,预防和化解返贫风险是“后扶贫时代”的工作重点。基于区域与个体尺度融合的新视角,运用BP神经网络法、熵值法和偏相关分析法对六盘山、秦巴山和大别山三大集中连片特困区进行返贫脆弱性评价与影响因素分析。研究发现：（1）三大集中连片特困区返贫脆弱度大致呈现由西向东递减的空间格局;（2）三个典型县区域和个体返贫脆弱性评价结果均显示古浪县>新县>栾川县;（3）高返贫风险县域中,高生态暴露度特征最为显著,而高返贫风险家庭中,生计动力不足特征最为明显;（4）区域返贫脆弱性主导因子为自然环境禀赋和经济发展水平,个体返贫脆弱性主导因子则为家庭劳动力综合素质、家庭收入、生计来源多样性、家庭成员健康状况和婚姻成本等。     

Predicting geogenic groundwater fluoride contamination throughout China

Endemic fluorosis exists in almost all provinces of China. The long-term ingestion of groundwater containing high concentrations of fluoride is one of the main causes of fluorosis. We used artificial neural network to model the relationship between groundwater fluoride concentrations from throughout China and environmental variables such as climatic, geological. and soil parameters as proxy predictors. The results show that the accuracy and area under the receiver operating characteristic curve of the model in the test dataset are 80.5% and 0.86%, respectively, and climatic variables are the most effective predictors. Based on the artificial neural network model, a nationwide prediction risk map of fluoride concentrations exceeding 1.5 mg/L with a 0.5 × 0.5 arc minutes resolution was generated. The high risk areas are mainly located in western provinces of Xinjiang, Tibet, Qinghai, and Sichuan, and the northern provinces of Inner Mongolia, Hebei and Shandong. The total number of people estimated to be potentially at risk of fluorosis due to the use of untreated high fluoride groundwater as drinking water is about 89 million, or 6% of the population. The high fluoride groundwater risk map helps the authorities to prioritize areas requiring mitigation measures and thus facilitates the implementation of water improvement and defluoridation projects.     

Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

Spatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.     

Radical,reformist, and garden-variety neoliberal: coming to terms with urban agriculture's contradictions

For many activists and scholars, urban agriculture in the Global North has become synonymous with sustainable food systems, standing in opposition to the dominant industrial agri-food system. At the same time, critical social scientists increasingly argue that urban agriculture programmes, by filling the void left by the “rolling back” of the social safety net, underwrite neoliberalisation. I argue that such contradictions are central to urban agriculture. Drawing on existing literature and fieldwork in Oakland, CA, I explain how urban agriculture arises from a protective counter-movement, while at the same time entrenching the neoliberal organisation of contemporary urban political economies through its entanglement with multiple processes of neoliberalisation. By focusing on one function or the other, however, rather than understanding such contradictions as internal and inherent, we risk undermining urban agriculture's transformative potential. Coming to terms with its internal contradictions can help activists, policy-makers and practitioners better position urban agriculture within coordinated efforts for structural change, one of many means to an end rather than an end unto itself.