Architectural Design of Electricity Power Consumption Misuse Detection Based on Light Gradient Boosting Machine Using Blockchain Technology DOI
Soiful Hadi, Wahyul Amien Syafei, Adi Wibowo

et al.

Published: Dec. 20, 2023

This research focuses on blockchain architecture design for Electricity misuse detection is a critical concern in contemporary energy management systems. Detecting and preventing unauthorized or improper usage poses significant challenge due to the complex diverse nature of consumption data. improving accuracy electricity through utilization LightGBM classification model machine learning. Light Gradient Boosting Machine (LightGBM) methods are employed their efficiency, accuracy, ability handle imbalanced datasets. Evaluation metrics, such as AUC (Area Under Curve), used assess performance model. The results demonstrate that classifier exhibits superior accurately detecting instances misuse. It achieves an impressive 84.19 % indicating its effectiveness identifying flagging events. By leveraging LightBGM algorithms, valuable insights can be obtained decision-making processes, aiding improvement strategies overall performance. analysis also identifies key factors influencing Block chain assist both providers policymakers developing effective measures curb decentralized manner so security guaranteed.

Language: Английский

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management DOI Creative Commons

Maria Drogkoula,

Konstantinos Kokkinos, Nicholas Samaras

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(22), P. 12147 - 12147

Published: Nov. 8, 2023

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain water resource management. Environmental issues, such as climate change ecosystem destruction, pose significant threats to humanity planet. Addressing these challenges necessitates sustainable management increased efficiency. Artificial intelligence (AI) ML technologies present promising solutions this regard. By harnessing AI ML, we can collect analyze vast amounts data from diverse sources, remote sensing, smart sensors, social media. enables real-time monitoring decision making applications, including irrigation optimization, quality monitoring, flood forecasting, demand enhance agricultural practices, distribution models, desalination plants. Furthermore, facilitates integration, supports decision-making processes, enhances overall sustainability. However, wider adoption faces challenges, heterogeneity, stakeholder education, high costs. To provide an management, research focuses on core fundamentals, major (prediction, clustering, reinforcement learning), ongoing issues offer new insights. More specifically, after in-depth illustration algorithmic taxonomy, comparative mapping all specific tasks. At same time, include tabulation works along with some concrete, yet compact, descriptions objectives at hand. leveraging tools, develop plans address world’s supply concerns effectively.

Language: Английский

Citations

51
Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models DOI
P. M. Huang,

Mengyao Hou,

Tong Sun

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 442, P. 141152 - 141152

Published: Feb. 1, 2024

Language: Английский

Citations

12
Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review DOI
Afzal Ahmed Soomro, Ainul Akmar Mokhtar,

Hilmi Hussin

et al.

Engineering Failure Analysis, Journal Year: 2023, Volume and Issue: 155, P. 107747 - 107747

Published: Nov. 3, 2023

Language: Английский

Citations

19
Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models DOI Creative Commons
Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Masdi Muhammad

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103233 - 103233

Published: Oct. 1, 2024

Language: Английский

Citations

4
A self-organizing map-based approach for groundwater model parameter identification DOI
Lixin Zhao, Hongyan Li,

Wenquan Yu

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Language: Английский

Citations

0
A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential DOI Creative Commons
Viet‐Ha Nhu, Duong Cao Phan,

Pham Viet Hoa

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 26344 - 26363

Published: Jan. 1, 2024

Groundwater spring plays a crucial role in human life, including water resource management and planning; therefore, developing accurate prediction models for groundwater potential mapping is essential. The objective of this research to introduce confirm new modeling approach based on TensorFlow Deep Neural Networks (TF-DNN) multisource geospatial data spatial potential, with case study the tropical province central highland Vietnam. For task, TF-DNN model structure three hidden layers 32 neurons each was established; therein, Adaptive Moment Estimation (ADAM) algorithm used as an optimizer, whereas Rectified Linear Unit (ReLU) activation function, sigmoid transfer function. A database area, consisting 733 locations 12 influencing factors, prepared ArcGIS Pro. Then, it develop verify model. Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, Classification Regression Trees were benchmark comparison. results demonstrate that proposed (Accuracy = 80.5%, F-score 0.797, AUC 0.864) achieves high global performance, outperforming models. Thus, represents novel effective tool spatially predicting mapping. map generated has assist provincial authorities formulating strategies concerning socio-economic development.

Language: Английский

Citations

3
A high-precision and interpretability-enhanced direct inversion framework for groundwater contaminant source identification using multiple machine learning techniques DOI

Liuzhi Zhu,

Wenxi Lu,

Chengming Luo

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133237 - 133237

Published: April 1, 2025

Language: Английский

Citations

0
Architectural Design of Electricity Power Consumption Misuse Detection Based on Light Gradient Boosting Machine Using Blockchain Technology DOI
Soiful Hadi, Wahyul Amien Syafei, Adi Wibowo

et al.

Published: Dec. 20, 2023

This research focuses on blockchain architecture design for Electricity misuse detection is a critical concern in contemporary energy management systems. Detecting and preventing unauthorized or improper usage poses significant challenge due to the complex diverse nature of consumption data. improving accuracy electricity through utilization LightGBM classification model machine learning. Light Gradient Boosting Machine (LightGBM) methods are employed their efficiency, accuracy, ability handle imbalanced datasets. Evaluation metrics, such as AUC (Area Under Curve), used assess performance model. The results demonstrate that classifier exhibits superior accurately detecting instances misuse. It achieves an impressive 84.19 % indicating its effectiveness identifying flagging events. By leveraging LightBGM algorithms, valuable insights can be obtained decision-making processes, aiding improvement strategies overall performance. analysis also identifies key factors influencing Block chain assist both providers policymakers developing effective measures curb decentralized manner so security guaranteed.

Language: Английский

Citations

0