Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks DOI

S Ramya,

S Srinath,

Pushpa Tuppad

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(12)

Published: Nov. 27, 2024

Language: Английский

Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods DOI
Johnson C. Agbasi, Mahamuda Abu, Johnbosco C. Egbueri

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: June 21, 2024

Language: Английский

Citations

17
Evidential-bio-inspired algorithms for modeling groundwater total hardness: A pioneering implementation of evidential neural network for feature selection in water resources management DOI Creative Commons
A. G. Usman, Abdulhayat M. Jibrin, Sagiru Mati

et al.

Environmental Chemistry and Ecotoxicology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

2
Application of machine learning in delineating groundwater contamination at present times and in climate change scenarios DOI

Tridip Bhowmik,

Soumyajit Sarkar,

Somdipta Sen

et al.

Current Opinion in Environmental Science & Health, Journal Year: 2024, Volume and Issue: 39, P. 100554 - 100554

Published: May 5, 2024

Language: Английский

Citations

6
Robust variable-order fractional PID-LP fuzzy controller for Automatic Voltage Regulator systems DOI
Mohsen Ahmadnia, Ahmad Hajipour, Hamidreza Tavakoli

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112268 - 112268

Published: Sept. 1, 2024

Language: Английский

Citations

6
Evolutionary Neural Network-Based Online Ecological Governance Monitoring of Industrial Water Pollution DOI Creative Commons

Ying Zhao

International Journal of Swarm Intelligence Research, Journal Year: 2025, Volume and Issue: 16(1), P. 1 - 23

Published: Feb. 26, 2025

This paper proposes ENNOEIGS, an evolutionary neural network-based online ecological industrial governance system that integrates advanced architectures with optimization for robust pollution monitoring. The framework combines convolutional networks dimensional reduction of sensor data, external attention mechanisms discovering pattern correlations, and long short-term memory modeling the spatiotemporal evolution contaminants. A genetic algorithm continuously optimizes network parameters, enabling adaptation to changing conditions. Experimental validation using wastewater monitoring data demonstrates ENNOEIGS's superior performance, achieving a 94.8% anomaly detection rate 2.3% false alarms, outperforming existing approaches. reduces mean modified absolute error 0.028 mg/L while maintaining faster convergence during training.

Language: Английский

Citations

0
Comparison of extreme gradient boosting, deep learning, and self-organizing map methods in predicting groundwater depth DOI
Vahid Gholami, Mohammad Reza Khaleghi, E. Taghvaye Salimi

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(7)

Published: March 21, 2025

Language: Английский

Citations

0
Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran DOI
Mohammad Bashirgonbad, Behnoush Farokhzadeh, Vahid Gholami

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(38), P. 50427 - 50442

Published: Aug. 2, 2024

Language: Английский

Citations

2
Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater DOI
Vahid Gholami, Hossein Sahour, Mohammad Reza Khaleghi

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(5), P. 4789 - 4809

Published: Jan. 29, 2024

Language: Английский

Citations

1
An Intelligent Cloud-Based IoT-Enabled Multimodal Edge Sensing Device for Automated, Real-Time, Comprehensive, and Standardized Water Quality Monitoring and Assessment Process Using Multisensor Data Fusion Technologies DOI
Mohsen Mohammadi, Ghiwa Assaf, Rayan H. Assaad

et al.

Journal of Computing in Civil Engineering, Journal Year: 2024, Volume and Issue: 38(6)

Published: July 29, 2024

Language: Английский

Citations

1
Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis DOI Creative Commons

R. Siddthan,

Shanthi PM

Journal of Machine and Computing, Journal Year: 2024, Volume and Issue: unknown, P. 702 - 721

Published: July 5, 2024

Human actions and changing weather patterns are contributing to the growing demand for groundwater resources. Nevertheless, evaluating quality of is crucial. Nitrate a significant water contaminant that can lead blue-baby syndrome or methemoglobinemia. Therefore, it necessary assess level nitrate in groundwater. Current methods involve integrating into models. The inappropriate datasets, lack performance, other constraints limitations current methods. Ground dataset used pre-processed data’s. Selected data’s feature extracted associated with rule ranking. In suggested model, use associative mining technique has been implemented address these challenges levels method ranking carried out using association divide datasets. split gini indexing algorithm introduced proposed model data classification. Split Gini Indexing decision tree induction build trees classification tasks. It based on impurity measure, which measures heterogeneity dataset. classified Naïve Bayes, SVM, KNN algorithms. approach's efficiency evaluated by calculating performance metrics such as precision, accuracy, F1-score, recall values. research attains an improved accuracy 0.99, demonstrating enhanced performance.

Language: Английский

Citations

0