Adaptive self-calibrated soft sensor for reliable nutrient measurement in rivers: Two-stage stacked autoencoder with densely connected fusion network

自编码 融合 阶段(地层学) 传感器融合 软传感器 营养物 计算机科学 环境科学 人工智能 模式识别(心理学) 遥感 生物 人工神经网络 生态学 地质学 语言学 过程(计算) 操作系统 哲学 古生物学
作者
Abdulrahman H. Ba-Alawi,Hanaa Aamer,Mohammed A. Al‐masni,ChangKyoo Yoo
出处
期刊:Journal of water process engineering [Elsevier BV]
卷期号:63: 105494-105494 被引量:7
标识
DOI:10.1016/j.jwpe.2024.105494
摘要

A soft sensor effectively estimates concentrations of total nitrogen (TN) and total phosphorus (TP) in rivers by utilizing easily measurable variables. However, in practical applications, the malfunction in sensors measuring easy-to-measure variables causes a deficiency in the developed TN and TP soft sensors. This study proposes an adaptive dual-stage soft sensor model (FAE-DNet) by stacking a fusion autoencoder (FAE) with a densely connected network (DNet) to estimate TN and TP reliably. In the first stage, a dataset consisting of ten biological-chemical variables with faulty measurements was self-calibrated using the FAE model. Subsequently, the second stage utilized the self-calibrated sensor data as input to the DNet to predict the TN and TP effectively. Furthermore, an explainable artificial intelligence (XAI) analysis was employed to elucidate the performance of the developed deep AI soft sensor model. The first-stage, FAE model, effectively handled faulty measurements, with low MSE values: 0.0913 for electrical conductivity (EC) and 0.1571 for dissolved oxygen (DO). In the second stage with DNet, nutrient prediction showed a superior R 2 value of 0.9557. However, the prediction showed a very poor performance with an R 2 value of 0.0749 when faulty data were utilized as input to the DNet without calibration using the FAE, highlighting the reliability of the two-stage FAE-DNet for precise nutrient estimation. Thus, the proposed FAE-DNet model provides advanced water quality monitoring through a self-calibrated soft sensor that accurately predicts TN and TP, making it a promising tool for monitoring waterbodies. • A dual-stage DL model based soft sensor for water nutrients monitoring was newly proposed. • First-stage based on FAE outperformed in reconstructing faulty measurements (MSE = 0.0913). • Second-stage based on DNet showed explainable and superior prediction of nutrients (R2 = 0.9557). • Residual error decreases by 89.44 % and 50.68 % in calibrated case compared to faulty case. • DNet based soft sensor outperformed, DNN, RF, and XGBoost models in nutrients prediction.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lkk完成签到,获得积分10
2秒前
无辜妙海完成签到,获得积分10
2秒前
SciEngineerX完成签到,获得积分10
3秒前
活力书包完成签到 ,获得积分10
6秒前
法桐落梦完成签到,获得积分10
10秒前
四十四次日落完成签到 ,获得积分10
13秒前
tmobiusx完成签到,获得积分10
14秒前
wmc1357完成签到,获得积分10
19秒前
LILYpig完成签到 ,获得积分10
21秒前
大气夜山完成签到 ,获得积分10
23秒前
23秒前
一枝完成签到 ,获得积分10
23秒前
Jason完成签到 ,获得积分10
24秒前
量子星尘发布了新的文献求助10
29秒前
xmqaq完成签到,获得积分10
29秒前
殷勤的紫槐完成签到,获得积分10
30秒前
琳琳完成签到 ,获得积分10
30秒前
小蘑菇应助科研通管家采纳,获得10
32秒前
舒心的青亦完成签到 ,获得积分10
34秒前
卫卫完成签到 ,获得积分10
34秒前
等待的幼晴完成签到,获得积分10
34秒前
瘦瘦的铅笔完成签到 ,获得积分10
35秒前
SSDlk发布了新的文献求助10
37秒前
默默完成签到 ,获得积分10
37秒前
氟锑酸完成签到 ,获得积分10
39秒前
忞航完成签到 ,获得积分10
42秒前
Silence完成签到 ,获得积分10
44秒前
贰鸟应助王富贵采纳,获得10
47秒前
Smoiy完成签到 ,获得积分10
50秒前
weng完成签到,获得积分10
51秒前
笑笑完成签到,获得积分10
52秒前
pagemao完成签到 ,获得积分10
52秒前
53秒前
mbl2006完成签到 ,获得积分10
58秒前
背书强完成签到 ,获得积分10
58秒前
feihua1完成签到 ,获得积分10
59秒前
小亮哈哈完成签到,获得积分0
1分钟前
Wang发布了新的文献求助10
1分钟前
keleboys完成签到 ,获得积分10
1分钟前
nine2652完成签到 ,获得积分10
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
A new approach to the extrapolation of accelerated life test data 1000
Coking simulation aids on-stream time 450
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 390
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 360
Novel Preparation of Chitin Nanocrystals by H2SO4 and H3PO4 Hydrolysis Followed by High-Pressure Water Jet Treatments 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4015603
求助须知:如何正确求助?哪些是违规求助? 3555597
关于积分的说明 11318138
捐赠科研通 3288782
什么是DOI,文献DOI怎么找? 1812284
邀请新用户注册赠送积分活动 887882
科研通“疑难数据库(出版商)”最低求助积分说明 812015