Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm DOI Creative Commons

Yuzhen Su,

Weichuan Ye,

Kai Yang

и другие.

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Окт. 16, 2023

Traditional linear regression and neural network models demonstrate suboptimal fit lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable accurate model essential for these challenges. Notably, maximum information coefficient was employed initially to discern non-linear correlation between nineteen factors influencing five control indicators. Additionally, random forest algorithm elucidated primary governing quality. hybrid model, integrating particle swarm optimization with least square support vector machine, devised predict based on factors. Concurrently, a combining relevance machine developed, focusing The outcomes indicate that identified principal quality, corroborated by analysis via coefficient. when accounting all factors, comparable optimization-least surpassed both conventional models. error forest-relevance notably less than sole index being under 5%. intricate variation pattern influenced numerous unveiled. advanced circumvents deficiencies seen in findings furnish valuable insights management.

Язык: Английский

Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model DOI
Qingqing Tian,

Wei Luo,

Lei Guo

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 59, С. 105052 - 105052

Опубликована: Март 1, 2024

Язык: Английский

Процитировано

21
A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India DOI Creative Commons
Nagalapalli Satish, Jagadeesh Anmala,

K. Rajitha

и другие.

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102500 - 102500

Опубликована: Янв. 28, 2024

The importance of water quality models has increased as their inputs are critical to the development risk assessment framework for environmental management and monitoring rivers. However, with advent a plethora recent advances in ML algorithms better predictions possible. This study proposes causal effect model by considering climatological such temperature precipitation along geospatial information related agricultural land use factor (ALUF), forest (FLUF), grassland usage (GLUF), shrub (SLUF), urban (ULUF). All these factors included input data, whereas four Stream Water Quality parameters (SWQPs) Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, Dissolved (DO) from 2019 2021 taken outputs predict Godavari River Basin quality. In preliminary investigation, out SWQPs, nitrate's coefficient variation (CV) is high, revealing close association climate practices across sampling stations. authors' earlier study, using single-layer Feed-Forward Neural Network (FFNN) showed improved performance predicting cause linked metrics. To achieve prediction, stacked ANN meta-model nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive (ADB), Decision Tree (DT), Highest (HGB), Light Method (LGBM), (GB), were compared this study. According study's findings, outperformed stand-alone FFNN same dataset superior predictive capabilities terms accuracy forecasting variable interest. For instance, during testing, determination (R2) (BOD) 0.72 0.87. Furthermore, Artificial (ANN) meta that was reinforced (ET) base performed than individual (from R2 = 0.87 0.91 BOD testing). By new framework, effort hyperparameter tuning can be minimized.

Язык: Английский

Процитировано

18
Coupling coordination degree analysis and spatiotemporal heterogeneity between water ecosystem service value and water system in Yellow River Basin cities DOI Creative Commons

Donghai Yuan,

Manrui Du,

Chenling Yan

и другие.

Ecological Informatics, Год журнала: 2023, Номер 79, С. 102440 - 102440

Опубликована: Дек. 18, 2023

Accelerated urbanization has caused encroachment on urban water ecological land in China's Yellow River basin, resulting a strong disturbance of ecosystem service functions and increasingly serious environmental problems. In this study, two entities—water value (WESV) the system—are identified, to investigate interactions between WESV systems their subsystems six basin cities (Lanzhou, Yinchuan, Hohhot, Xi'an, Zhengzhou, Jinan) from 2005 2020. First, integrated level system each city is calculated using modified developed method equivalence factor per unit area entropy method, respectively. Then, coupling coordination relationship are revealed by degree model (CCDM) Geographically Temporally Weighted Regression (GTWR). The results show that: 1) both basically shows an increasing trend, hydrological regulation function dominates functions, comprehensive evaluation environment generally higher than that other system's subsystems. 2) gradually rose extreme incoordination coordination, (CCD) resources also obvious upward but CCD safety developing more slowly. 3) where have greater positive impacts primarily focused Lanzhou while negative mainly located Yinchuan Zhengzhou. summary, planning decision-making or cities, it critical promote protection ecology high-quality development clearly understanding interaction services system, coordinating balancing systems.

Язык: Английский

Процитировано

29
Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture DOI

S Shreesha,

M. M. Manohara Pai, Radhika M. Pai

и другие.

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102287 - 102287

Опубликована: Сен. 7, 2023

Язык: Английский

Процитировано

17
A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors DOI Creative Commons
Seung‐Ho Kang,

In-Seon Jeong,

Hyeong-Seok Lim

и другие.

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102482 - 102482

Опубликована: Янв. 21, 2024

Caenorhabditis elegans is a representative organism whose DNA structure has been fully elucidated. It used as model for various analyses, including genetic functional analysis, individual behavioral and group analysis. Recently, it also studied an important bioindicator of water pollution. In previous studies, traditional machine learning methods, such the Hidden Markov Model (HMM), were to determine pollution identify pollutants based on differences in swimming behavior C. before after exposure chemicals. However, these models have low accuracy relatively high false-negative rate. This study proposes method detecting identifying types using Long Short-Term Memory (LSTM) model, deep suitable time-series data The activities each image frames are characterized by Branch Length Similarity (BLS) entropy profile. These BLS profiles converted into input vectors through additional preprocessing two clustering methods. We conduct experiments formaldehyde benzene at 0.1 mg/L each, with observation time intervals varying from 30 180 s. performance proposed compared that previously HMM approach variants LSTM models, Gated Recurrent Unit (GRU) Bidirectional (BiLSTM).

Язык: Английский

Процитировано

7
Predicting water quality in municipal water management systems using a hybrid deep learning model DOI

Wenxian Luo,

Leijun Huang,

Jiabin Shu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108420 - 108420

Опубликована: Апрель 23, 2024

Язык: Английский

Процитировано

6
Forecasting biochemical oxygen demand (BOD) in River Ganga: a case study employing supervised machine learning and ANN techniques DOI
Rohan Mishra,

Rupanjali Singh,

C. B. Majumder

и другие.

Sustainable Water Resources Management, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 16, 2025

Язык: Английский

Процитировано

0
GA-ML: enhancing the prediction of water electrical conductivity through genetic algorithm-based end-to-end hyperparameter tuning DOI

Muhammed Furkan Gül,

Halit Bakır

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

0
Dissolved Oxygen Prediction in the Dianchi River Basin with Explainable Artificial Intelligence based on Physical Prior Knowledge DOI
Tunhua Wu, Xi Chen, Jinghan Dong

и другие.

Environmental Modelling & Software, Год журнала: 2025, Номер 188, С. 106412 - 106412

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
Next-generation reservoir computing water quality prediction model based on the whale optimization algorithm DOI
Junyu Zhou, Lijun Pei, Zhiwei Zheng

и другие.

International Journal of Dynamics and Control, Год журнала: 2025, Номер 13(4)

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0