Water Quality Prediction of Min River Basin Based on GBDT-LSTM

doi:10.16258/j.cnki.1674-5906.2024.04.010

Ecology and Environment ›› 2024, Vol. 33 ›› Issue (4): 597-606.DOI: 10.16258/j.cnki.1674-5906.2024.04.010

• Research Article [Environmental Sciences] • Previous Articles Next Articles

Water Quality Prediction of Min River Basin Based on GBDT-LSTM

XIAO Yanglan¹^,²(), SHEN Huirou¹^,², XU Yihan¹^,², YOU Tiange¹^,²^,^*(), ZHENG Yijing¹^,², XIE Houzhan¹^,², NING Jing¹^,²

1. College of Computer and Information, University of Fujian Agriculture and Forestry, Fuzhou 350002, P. R. China
2. Fujian Statistical Information Research Center, Fuzhou 350002, P. R. China

Received:2024-01-19 Online:2024-04-18 Published:2024-05-31
Contact: YOU Tiange

基于GBDT-LSTM的闽江流域水质预测

肖扬岚¹^,²(), 沈惠柔¹^,², 许一涵¹^,², 尤添革¹^,²^,^*(), 郑艺婧¹^,², 谢候展¹^,², 宁静¹^,²

1.福建农林大学计算机与信息学院，福建福州 350002
2.福建省统计信息研究中心，福建福州 350002

通讯作者: 尤添革
作者简介:肖扬岚（2000年生），女，硕士研究生，主要从事水生态与环境研究。E-mail: 1221153030@fafu.edu.cn
基金资助:
福建省社会科学规划项目(FJ2018B063)

Abstract

Abstract:

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 quality predictions. 4) The continuous increase in total nitrogen and permanganate index in water was mainly due to a large amount of industrial wastewater. It is recommended to strengthen the control of high-pollution and high-emission enterprises near the Min River Basin. Additionally, there should be a scientifically and reasonably timed and spatially distributed approach to industrial pollution emission and pollutant capacity management.

Key words: Min River Basin, Water quality prediction, water quality composite index, LSTM neural network, gradient boosting tree, water quality assessment

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。

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

CLC Number:

X52
X832

XIAO Yanglan, SHEN Huirou, XU Yihan, YOU Tiange, ZHENG Yijing, XIE Houzhan, NING Jing. Water Quality Prediction of Min River Basin Based on GBDT-LSTM[J]. Ecology and Environment, 2024, 33(4): 597-606.

肖扬岚, 沈惠柔, 许一涵, 尤添革, 郑艺婧, 谢候展, 宁静. 基于GBDT-LSTM的闽江流域水质预测[J]. 生态环境学报, 2024, 33(4): 597-606.

Figures/Tables 11

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监测点	序号	所属河流	监测点	序号	所属河流	监测点	序号	所属河流	监测点	序号	所属河流
福州文山里	A	闽江下游	建瓯蓬墩	F	崇阳溪	延平浪石	K	富屯溪	古田水库	P	古田溪
连江琯头	B	闽江下游	坪洲大桥	G	南浦溪	延平南溪	L	尤溪	大田高才	Q	均溪
闽侯竹岐	C	闽江下游	将乐樟应	H	金溪	延平洋坑	M	闽江上游	建宁袁庄	R	濉溪
闽清雄江	D	闽江下游	南平水汾桥	I	沙溪	政和西津	N	松溪	斑竹渡溪口	S	沙溪
建瓯房村	E	建溪	武夷山兴田	J	崇阳溪	古田黄田	O	闽江中游	安砂水库下游	T	九龙溪

监测点	序号	所属河流	监测点	序号	所属河流	监测点	序号	所属河流	监测点	序号	所属河流
福州文山里	A	闽江下游	建瓯蓬墩	F	崇阳溪	延平浪石	K	富屯溪	古田水库	P	古田溪
连江琯头	B	闽江下游	坪洲大桥	G	南浦溪	延平南溪	L	尤溪	大田高才	Q	均溪
闽侯竹岐	C	闽江下游	将乐樟应	H	金溪	延平洋坑	M	闽江上游	建宁袁庄	R	濉溪
闽清雄江	D	闽江下游	南平水汾桥	I	沙溪	政和西津	N	松溪	斑竹渡溪口	S	沙溪
建瓯房村	E	建溪	武夷山兴田	J	崇阳溪	古田黄田	O	闽江中游	安砂水库下游	T	九龙溪

指标		单位	指标代码	指标		单位	指标代码
自然	水温	℃	x₁	社会	人均GDP	Yuan (RMB)	x₁₉
	pH值	－	x₂		常住人口	10⁴person	x₂₀
	溶解氧	mg·L^-1	x₃		人口密度	10⁴person ·km^-2	x₂₁
	电导率	µS·cm^-1	x₄		城市污水排放量	10⁴m³	x₂₂
	浊度	NTU	x₅		城镇生活废水中氨氮排放量	t	x₂₃
	高锰酸盐指数	mg·L^-1	x₆		工业废水中氨氮排放量	t	x₂₄
	氨氮	mg·L^-1	x₇		工业废水排放量	t	x₂₅
	总磷	mg·L^-1	x₈		农药使用量	t	x₂₆
	总氮	mg·L^-1	x₉		农用化肥施用量	t	x₂₇
	降水量	mg·L^-1	x₁₀		农用氮肥施用量	t	x₂₈
	平均气温	℃	x₁₁		农用磷肥施用量	t	x₂₉
	平均湿度	%	x₁₂		第一产业比重	%	x₃₀
	PM2.5	µg·m^-3	x₁₃		第二产业比重	%	x₃₁
	PM10	µg·m^-3	x₁₄		第三产业比重	%	x₃₂
	NO₂	µg·m^-3	x₁₅		城市污水处理厂集中处理率	%	x₃₃
	CO	µg·m^-3	x₁₆		生活垃圾无害化处理率	%	x₃₄
	SO₂	µg·m^-3	x₁₇		水土保持总投资	10⁴ Yuan (RMB)	x₃₅
	O₃	µg·m^-3	x₁₈		减少水土流失量	10⁴ t	x₃₆

指标		单位	指标代码	指标		单位	指标代码
自然	水温	℃	x₁	社会	人均GDP	Yuan (RMB)	x₁₉
	pH值	－	x₂		常住人口	10⁴person	x₂₀
	溶解氧	mg·L^-1	x₃		人口密度	10⁴person ·km^-2	x₂₁
	电导率	µS·cm^-1	x₄		城市污水排放量	10⁴m³	x₂₂
	浊度	NTU	x₅		城镇生活废水中氨氮排放量	t	x₂₃
	高锰酸盐指数	mg·L^-1	x₆		工业废水中氨氮排放量	t	x₂₄
	氨氮	mg·L^-1	x₇		工业废水排放量	t	x₂₅
	总磷	mg·L^-1	x₈		农药使用量	t	x₂₆
	总氮	mg·L^-1	x₉		农用化肥施用量	t	x₂₇
	降水量	mg·L^-1	x₁₀		农用氮肥施用量	t	x₂₈
	平均气温	℃	x₁₁		农用磷肥施用量	t	x₂₉
	平均湿度	%	x₁₂		第一产业比重	%	x₃₀
	PM2.5	µg·m^-3	x₁₃		第二产业比重	%	x₃₁
	PM10	µg·m^-3	x₁₄		第三产业比重	%	x₃₂
	NO₂	µg·m^-3	x₁₅		城市污水处理厂集中处理率	%	x₃₃
	CO	µg·m^-3	x₁₆		生活垃圾无害化处理率	%	x₃₄
	SO₂	µg·m^-3	x₁₇		水土保持总投资	10⁴ Yuan (RMB)	x₃₅
	O₃	µg·m^-3	x₁₈		减少水土流失量	10⁴ t	x₃₆

指标代码	x₃	x₄	x₅	x₆	x₇	x₈	x₉	x₁₀	x₁₇	x₁₈	WQI
x₃	1	2×10^-3	6.5×10^-2	7.2×10^-2	-5.2×10^-2	9.8×10^-2	-0.110	0.268^**	3×10^-2	-8.7×10^-2	0.198^*
x₄	2×10^-3	1	0.862^**	0.522^**	0.105	0.335^**	0.703^**	-0.281^**	-0.234^**	0.275^**	-0.647^**
x₅	6.5×10^-2	0.862^**	1	0.582^**	0.145	0.434^**	0.593^**	-0.147	-0.231^**	0.239^**	-0.689^**
x₆	7.2×10^-2	0.522^**	0.582^**	1	0.136	0.259^**	0.343^**	-6.4×10^-2	-1.5×10^-2	0.141	-0.530^**
x₇	-5.2×10^-2	0.105	0.145	0.136	1	0.428^**	0.375^**	0.149	0.248^**	-0.198^*	-0.465^**
x₈	9.8×10^-2	0.335^**	0.434^**	0.259^**	0.428^**	1	0.625^**	5×10^-2	-0.170^*	0.108	-0.661^**
x₉	-0.110	0.703^**	0.593^**	0.343^**	0.375^**	0.625^**	1	-0.332^**	-0.198^*	0.208^*	-0.808^**
x₁₀	0.268^{** 2)}	-0.281^**	-0.147	-6.4×10^-2	0.149	5×10^-2	-0.332^**	1	0.220^**	-0.242^**	0.215^*
x₁₇	3×10^-2	-0.234^**	-0.231^**	-1.5×10^-2	0.248^**	-0.170^*	-0.198^*	0.220^**	1	-0.393^**	0.184^*
x₁₈	-8.7×10^-2	0.275^**	0.239^**	0.141	-0.198^*	0.108	0.208^*	-0.242^**	-0.393^**	1	-0.196^*
WQI	0.198^{* 1)}	-0.647^**	-0.689^**	-0.530^**	-0.465^**	-0.661^**	-0.808^**	0.215^*	0.184^*	-0.196^*	1

Water Quality Prediction of Min River Basin Based on GBDT-LSTM

基于GBDT-LSTM的闽江流域水质预测

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指标代码	x₂₀	x₂₁	x₂₂	x₂₃	x₂₅	x₃₀	x₃₁	x₃₂	x₃₃	x₃₅	WQI
x₂₀	1	0.995^**	0.993^**	0.683^**	-0.656^**	-0.856^**	-0.268^**	0.734^**	0.276^**	0.717^**	-0.391^**
x₂₁	0.995^**	1	0.982^**	0.693^**	0.615^**	-0.883^**	-0.195^*	0.681^**	0.225^**	0.696^**	-0.365^**
x₂₂	0.993^**	0.982^**	1	0.621^**	0.716^**	-0.860^**	-0.283^**	0.752^**	0.280^**	0.742^**	-0.390^**
x₂₃	0.683^**	0.693^**	0.621^**	1	0.274^**	-0.626^**	7.2×10^-2	0.277^**	-7.5×10^-2	0.159	-0.254^**
x₂₅	-0.656^**	0.615^**	0.716^**	0.274^**	1	-0.678^**	-0.183^*	0.566^**	2.4×10^-2	0.549^**	-0.268^**
x₃₀	-0.856^**	-0.883^**	-0.860^**	-0.626^**	0.678^**	1	-0.179^**	-0.397^**	0.186^*	-0.537^**	0.236^**
x₃₁	-0.268^**	-0.195^*	-0.283^**	7.2×10^-2	-0.183^*	-0.179^**	1	-0.830^**	-0.903^**	-0.501^**	0.305^**
x₃₂	0.734^**	0.681^**	0.752^**	0.277^**	0.566^**	-0.397^**	-0.830^**	1	0.741^**	0.770^**	-0.417^**
x₃₃	0.276^**	0.225^**	0.280^**	-7.5×10^-2	2.4×10^-2	0.186^*	-0.903^**	0.741^**	1	0.504^**	-0.280^**
x₃₅	0.717^**	0.696^**	0.742^**	0.159	0.549^**	-0.537^**	-0.501^**	0.770^**	0.504^**	1	-0.354^**
WQI	-0.391^**	-0.365^**	-0.390^**	-0.254^**	-0.268^**	0.236^**	0.305^**	-0.417^**	-0.280^**	-0.354^**	1

水质指标	LSTM			GBDT-LSTM
水质指标	MAE	RMSE	r²	MAE	RMSE	r²
高锰酸盐指数	5.4×10^-2	0.121	0.869	3.8×10^-2	9.1×10^-2	0.899
氨氮	7×10^-3	1.7×10^-2	0.887	4×10^-3	1×10^-2	0.917
总磷	3×10^-3	7×10^-3	0.879	2×10^-3	5×10^-3	0.912
总氮	5.39×10^-2	0.128	0.867	3.2×10^-2	7.86×10^-2	0.914

[1]	LU Yanbo, CHEN Zhanfeng, LI Xiaofang. A Study on Water Quality Prediction Model of Cross-boundary Sections in Guangdong Province Based on GRU Improved with Particle Swarm Optimization [J]. Ecology and Environment, 2023, 32(9): 1673-1681.
[2]	CHENG Peng, SUN Mingdong, HAO Shaonan. Water Quality Assessment of Upstream Rivers of Guanting Reservoir Based on the Simplest Water Quality Index [J]. Ecology and Environment, 2023, 32(2): 372-380.