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| 1 | Water quality assessment in Qu River based on fuzzy water pollution index method显示文摘A fuzzy improved water pollution index was proposed based on fuzzy inference system and water pollution index. This method can not only give a comprehensive water quality rank,but also describe the water quality situation with a quantitative value, which is convenient for the water quality comparison between the same ranks. This proposed method is used to assess water quality of Qu River in Sichuan, China. Data used in the assessment were collected from four monitoring stations from 2006 to 2010. The assessment results show that Qu River water quality presents a downward trend and the overall water quality in 2010 is the worst. The spatial variation indicates that water quality of Nanbashequ section is the pessimal. For the sake of comparison, fuzzy comprehensive evaluation and grey relational method were also employed to assess water quality of Qu River. The comparisons of these three approaches' assessment results show that the proposed method is reliable. | Ranran Li Zhihong Zou Yan An | 2016 | Journal of Environmental Sciences2016，28，12： | 30 |
| 2 | 农村小型集中式供水水质的模糊数学评价显示文摘目的应用模糊数学综合评价方法评价农村小型集中式供水水质的卫生质量。方法随机抽取金华市15家农村小型集中式供水设施,按照GB 5750—2006《生活饮用水标准检验方法》检测其卫生相关指标,并依据WS/T 199—2001《公共场所卫生综合评价方法》对检测结果进行模糊数学综合评价。结果 15家农村小型集中式供水设施检测合格率为6.67%。检测项目中耐热大肠菌群合格率为26.67%,总大肠菌群合格率为6.67%,菌落总数、氟化物、氯化物、硫酸盐、溶解性总固体、铁、耗氧量、总硬度和硝酸盐合格率为100.00%。供水设施卫生质量经模糊隶属度评判,Ⅰ级(良好)、Ⅱ级(合格)和Ⅳ级(很差)的各有4家,Ⅲ级(较差)有3家,卫生质量模糊综合指数均<0.5。结论金华市农村小型集中式供水设施存在安全风险,总大肠菌群和耐热大肠菌群合格率低是造成供水合格率低的主要原因。有关部门应加强管理、安装消毒设备、规范消毒,提高水质合格率。 | 陈强 吴位新 王祚懿 盛微 何晓庆 | 2016 | 浙江预防医学2016，28，6： | 4 |
| 3 | Prediction of effluent concentration in a wastewater treatment plant using machine learning models显示文摘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. | Hong Guo Kwanho Jeong Jiyeon Lim Jeongwon Jo Young Mo Kim Jong-pyo Park Joon Ha Kim Kyung Hwa Cho | 2015 | Journal of Environmental Sciences2015，27，6： | 4 |
| 4 | 中国边境地区西北段饮用水质量的模糊数学评价显示文摘目的将模糊数学的方法引入到中国边境地区西北段饮用水质量的评价中,旨在将大量的监测结果归纳综合,得出总的结论,以深入、量化分析水质的综合卫生状况,为中国陆地边境地区西北段饮用水质量评价提供科学依据。方法按随机原则选取中国边境地区西北段的16个水源点,在现场调查和实验室检测的基础上,应用模糊数学方法,利用隶属度描述水质分类界限,经模糊矩阵复合运算,得出综合评价结果。结果水质总体良好,水质类别以Ⅰ类和Ⅱ类为主,但部分水样存在卫生安全隐患。结论应用模糊数学方法综合评价中国边境地区西北段饮用水质量,克服了以往用单一指标的不足,客观反映出饮用水质量的实际状况,评价方法具有较高的实用价值。 | 张建江 党荣理 马永红 田华 宋远新 贾继民 | 2014 | 中国卫生检验杂志2014，24，1： | 3 |
| 5 | 基于CMAES集成学习方法的地表水质分类显示文摘为了提高人民生活质量,政府部门不断加强水质管理,然而人工分类方法无法满足实时处理的需求,传统机器学习方法的分类准确率又不够高。集成学习使用多种学习算法来获得比单一学习算法更好的预测性能。首先,对集成学习进行概述,简要介绍了Bagging和Boosting算法,并提出基于协方差自适应调整的进化策略算法(CMAES)的集成学习方法。接着,介绍了数据处理方式、模型评估方法和评价指标。最后,用CMAES集成学习方法对逻辑回归、线性判别分析、支持向量机、决策树、完全随机树、朴素贝叶斯、K-邻近算法、随机森林、完全随机树林、深度级联森林十种模型进行集成。实验结果表明,CMAES集成学习方法优于所有其他模型,该方法将继续被应用到未来的研究之中。 | 陈兴国 徐修颖 陈康扬 杨光 | 2020 | 计算机科学与探索2020，14，3： | 3 |
| 6 | 新疆军区部队生活饮用水水质的模糊数学评价显示文摘目的应用模糊数学对新疆军区某部队生活饮用水水质进行综合评价,使其结果更加科学和客观,为新疆军区部队饮水安全管理提供科学依据。方法在现场调查和实验室检测的基础上,选择12个检测项目作为综合评价指标。建立模糊数学模型对新疆军区某部生活饮用水水质进行模糊综合评价。结果所调查部队生活饮用水水质类别以Ⅱ类为主,但少部分部队生活饮用水的溶解性总固体、总硬度、硝酸盐氮、氟化物、菌落总数和总大肠菌群等较高,归为Ⅲ类。结论新疆军区某部队生活饮用水水质总体上良好,但少部分部队生活饮用水存在安全隐患,应加强监管。模糊数学评价方法可客观、综合评价水质情况,具有较好的实用价值。 | 张建江 贾继民 田华 马永红 宋远新 张华 | 2013 | 解放军预防医学杂志2013，31，6： | 2 |
| 7 | 中国西北典型干旱缺水城市饮水安全的模糊数学评价显示文摘目的应用模糊数学的方法,建立一种适于描述乌鲁木齐市生态系统饮用水水质的逻辑模式,对乌鲁木齐市生态系统饮用水水质的安全状况进行更加科学、全面的分析。方法于2013年6月按照随机原则选取乌鲁木齐市9个采样点,将采集的水样并进行实验室检测后应用模糊数学方法,利用隶属度描述水质分类界限,经模糊矩阵复合运算。结果 9个采样点中,属于Ⅰ类水质的有2个(22.2%),属于Ⅱ类水质的有4个(44.4%),属于Ⅲ类水质的有3个(33.3%),乌鲁木齐市生态系统饮用水水质总体良好,但部分水样有安全隐患。结论应用模糊数学方法综合评价乌鲁木齐市生态系统中饮用水水质的安全状况,较常用的单因子水质评价方法能更全面地反映水环境质量受各监测指标的综合影响。 | 张建江 贾继民 马永红 田华 邱尔臣 宋远新 | 2015 | 职业与健康2015，0，9： | 1 |
| 8 | 基于SMOTE-GA-CatBoost算法的全国地表水水质分类评价显示文摘针对地表水分类评价中水污染特征空间的高冲突性以及水质类别的不均衡性等问题,以7项地表水水质指标为水质评价因子,采用SMOTE过采样技术结合遗传算法和CatBoost模型对全国主要江河和重要湖库分别进行水质分类评价,并与其他4种改进集成算法进行对比.结果表明:SMOTE预处理有效改善样本类别的不均衡性,提高CatBoost模型对少数类水质样本分类的准确性;遗传算法调参有效提高CatBoost模型的收敛速度和分类精度,优化了模型的分类性能;SMOTE-GA-CatBoost模型对江河和湖库的水质分类效果均优于其他4种改进集成分类模型,其对江河水水质分类的准确率、精确率、召回率、F1分别为97.7%、97.8%、96.1%、96.9%,对湖库水水质分类的准确率、精确率、召回率、F1分别为96.7%、96.2%、95.4%、95.8%,该模型可以实现不同水域的水质分类评价. | 徐玲 景向楠 杨英 李卫华 刘怡心 严国兵 | 2023 | 中国环境科学2023，43，7： | 1 |
| 9 | Application of adaptive neuro-fuzzy inference system to predict draft and energy requirements of a disk plow显示文摘The energy and draft requirements of a disk plow have been recognized as essential factors when attempting to correctly match it with tractor power.This study examines the possible of using an adaptive neuro-fuzzy inference system(ANFIS)approach and its performance compared to a multiple linear regression(MLR)model to determine the energy and draft requirements of a disk plow.A total of 133 data patterns were obtained by conducting experiments in the field and from the literature.Of these 133 data points,121 were arbitrarily selected and used for training,and the remaining 12 were used for testing the models.The input variables were plowing depth,plowing speed,soil texture index,initial soil moisture content,initial soil bulk density,disk diameter,disk angle,and disk tilt angle,and output variable was draft of the disk plow.Four membership functions were used with ANFIS:a triangular membership function,generalized bell-shaped membership function,trapezoidal membership function,and Gaussian curve membership function.An evaluation of the outcomes of the ANFIS and MLR modeling shows that the triangular membership function performed better than the other functions.When the ANFIS model draft predictions were compared to the measured values,the average relative error was-1.97%.A comparison of the ANFIS model with other approaches showed that the energy and draft requirements of the disk plow could be estimated with satisfactory accuracy. | Naji Mordi N.Al-Dosary Saad A.Al-Hamed Abdulwahed M.Aboukarima | 2020 | International Journal of Agricultural and Biological Engineering2020，13，2： | 0 |
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