摘要
闽江作为福建省最大的河流,其生态保护对维护全省水生态环境而言意义重大。为进一步利用水质评价和预测方法对水质状况进行有效分析,选取闽江流域2017年1月-2023年8月共20处水质监测站数据作为研究对象,采用水质综合指数法对各站点水质状态进行判断;比较LSTM模型和GBDT-LSTM模型的拟合结果,并对各指标数据进行预测。结果表明,1)闽江流域各监测点除总氮外的水质指标均呈现较好趋势,各监测点的总氮浓度存在差异,整体情况较差,其中位于三明和南平市的河段由于当地重工业发达,导致水体中的硝态氮不断增加,进而致使河段内总氮浓度过高。2)流域水质的WQI值呈现逐年上升的趋势,水质状况普遍处于中等及以上水平,仅有少数监测点的水质状况处于很差状态,从中上游携带的泥沙等固体的堆积导致了连江琯头的水质情况较差;沙县斑竹溪渡口由于位于三明和南平的交界处,且沿岸分布较多重工业城市,故水质状态略差;相较于南平和三明的大型重工业企业,位于古田县的监测点以农业、轻工业为主,对水质指标的影响相对较小,水质状况因此较好。3)采用GBDT对变异程度较高的各水质指标在预测模型中的重要性进行排序,发现GBDT-LSTM混合模型的拟合效果相较于LSTM模型更好,更有利于对水质状况进行精确地预测。4)水体中总氮、高锰酸盐指数等含量的不断增加主要源于大量的工业废水,建议加强对临近闽江流域高污染高排放企业的控制,科学合理地实现工业污染排放和污染物容量在时空上的合理分配。
As the largest river in Fujian Province,the ecological protection of the Min River is of great significance to the maintenance of the province's water ecosystem.In order to further utilize the water quality evaluation and prediction methods to effectively analyze the water quality status,data from a total of 20 water quality monitoring stations in the Min River Basin from January 2017 to August 2023 were selected as the research object.The water quality composite index method was used to judge the status of water quality at each station.The fitting results of the LSTM model and GBDT-LSTM model were compared,and the prediction of the data of each index was carried out.The results showed that:1)all water quality indexes,except for total nitrogen,showed improvement at each monitoring site in the Min River Basin.However,there were differences in total nitrogen concentrations across monitoring sites.The overall situation was poor,particularly in river sections located in Sanming and Nanping cities,where the development of local heavy industry had led to a continuous increase in nitrate nitrogen levels and high concentrations of total nitrogen.2)The WQI value of water quality in the basin showed an increasing trend year by year,with most sites maintaining medium or better water quality.Only a few monitoring points exhibited very poor water quality.The accumulation of solids,such as sediment carried from the middle and upper reaches of the river,contributed to the poor water quality of Guantou in Lianjiang.The Banzhuxi Ferry in Shaxian also showed slightly poor water quality due to the influence of nearby heavy industrial cities.In contrast,monitoring sites in Gutian,which were influenced more by agriculture and light industries,displayed better water quality conditions.3)The GBDT was used to rank the importance of various water quality indicators with high degree of variation in the prediction model.The fitting effect of the hybrid model of GBDT-LSTM was better than that of the LSTM model,aiding in more accurate water
作者
肖扬岚
沈惠柔
许一涵
尤添革
郑艺婧
谢候展
宁静
XIAO Yangan;SHEN Huirou;XU Yihan;YOU Tiange;ZHENG Yijing;XIE Houzhan;NING Jing(College of Computer and Information,University of Fujian Agriculture and Forestry,Fuzhou 350002,P.R.China;Fujian Statistical Information Research Center,Fuzhou 350002,P.R.China)
出处
《生态环境学报》
CSCD
北大核心
2024年第4期597-606,共10页
Ecology and Environmental Sciences
基金
福建省社会科学规划项目(FJ2018B063)。
关键词
闽江流域
水质预测
水质综合指数
长短期记忆神经网络
梯度提升树
水质评价
Min River Basin
Water quality prediction
water quality composite index
LSTM neural network
gradient boosting tree
water quality assessment
