Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

Environment Development and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Июнь 21, 2024

Язык: Английский

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Evidential-bio-inspired algorithms for modeling groundwater total hardness: A pioneering implementation of evidential neural network for feature selection in water resources management

Environmental Chemistry and Ecotoxicology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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

Current Opinion in Environmental Science & Health, Год журнала: 2024, Номер 39, С. 100554 - 100554

Опубликована: Май 5, 2024

Язык: Английский

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Robust variable-order fractional PID-LP fuzzy controller for Automatic Voltage Regulator systems

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112268 - 112268

Опубликована: Сен. 1, 2024

Язык: Английский

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Evolutionary Neural Network-Based Online Ecological Governance Monitoring of Industrial Water Pollution

International Journal of Swarm Intelligence Research, Год журнала: 2025, Номер 16(1), С. 1 - 23

Опубликована: Фев. 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.

Язык: Английский

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Comparison of extreme gradient boosting, deep learning, and self-organizing map methods in predicting groundwater depth

Environmental Earth Sciences, Год журнала: 2025, Номер 84(7)

Опубликована: Март 21, 2025

Язык: Английский

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Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(38), С. 50427 - 50442

Опубликована: Авг. 2, 2024

Язык: Английский

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Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater

Natural Hazards, Год журнала: 2024, Номер 120(5), С. 4789 - 4809

Опубликована: Янв. 29, 2024

Язык: Английский

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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

Journal of Computing in Civil Engineering, Год журнала: 2024, Номер 38(6)

Опубликована: Июль 29, 2024

Язык: Английский

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

Journal of Machine and Computing, Год журнала: 2024, Номер unknown, С. 702 - 721

Опубликована: Июль 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.

Язык: Английский

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