Auto-Grouping Sedimentation Using Unsupervised Based Clustering Techniques

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 4

Year of Publication : 2024

Authors : Radhika Surampudi, R. Kumudham

10.14445/23488549/IJECE-V11I4P102

How to Cite?

Radhika Surampudi, R. Kumudham, "Auto-Grouping Sedimentation Using Unsupervised Based Clustering Techniques," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 4, pp. 9-23, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I4P102

Abstract:

A Machine Learning (ML) algorithm plays an important role in the prediction of inaccuracies in several fields, such as medicine, computer science, along underwater particle sedimentation. Hence, in this research work, the authors implemented various clustering methods for grouping the sediment particles such as mud, sand50, gravel50, rock 10 cm, rock 50cm, surface carbon, and nitrogen in the underwater sea automatically. This research focuses on the application of unsupervised machine learning, specifically clustering techniques, to automate the grouping of underwater sediment particles. The research highlights the utilization of K-means Clustering and BIRCH Clustering, introducing a novel contribution in the form of a Hybrid Clustering approach that integrates the benefits of both methods. This hybridization is designed to refine and enhance clustering results, presenting a promising solution for the automation of sediment analysis in underwater environments. To predict the performance of various unsupervised machine learning-based clustering algorithms, metrics like Calinski Harabasz, Silhouette Score, Mathew’s Correlation Score, Davies Bouldin, Hamming loss, and Cohen Kappa score with n=7 are evaluated in underwater sediment particles grouping. Among several clustering techniques, the proposed hybrid approach outperforms in clustering of sediment articles based on the Silhouette score.

Keywords:

Auto-grouping, Sedimentation, Unsupervised clustering methods, Hybrid clustering, Machine Learning.

References:

[1] W.K. Stewart, M. Jiang, and M. Marra, “A Neural Networks Approach to Classifications of Side Scans Sonar Images from Midocean Ridges Areas,” IEEE Journals of Oceanic Engineering, vol. 19, no. 2, pp. 214-224, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xiaowen Luo et al., “Sediments Classifications of Small-Sizes Sea Bed Acoustics Image Using Convolution Neural Network,” IEEE Access, vol. 7, pp. 98331-98339, 2019.
[CrossRef] [Publisher Link]
[3] Zaki Abda et al., “Suspended Sediments Loads Simulations during Floods Event Using Intelligence System: Case Study on the Semiarid Region of Mediterranean Basins,” Water, vol. 13, no. 24, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Dillip K. Ghose, and Sandeep Samantaray, “Sedimentations Process and Its Assessments through Integrated Sensors Network and Machine Learning Process,” Computational Intelligence in Sensor Networks, pp. 473-488, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Maryam Asadi et al., “Predictions of Rivers Suspended Sediments Loads Using Machines Learning Model and Geo-Morphometric Parameter,” Arabian Journals of Geoscience, vol. 14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Vahid Nourani, Huseyin Gokcekus, and Gebre Gelete, “Estimations of Suspended Sediments Loads Using Artificials Intelligences-Based Ensembled Models,” Complexity, vol. 2021, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mohammad Zounemat-Kermani et al., “On the Complexity of Sediments Loads Modelling using Integrative Machines Learning: An Applications to the Great Rivers of LoÃza in Puerto-Rico,” Journals of Hydrology, vol. 585, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Banu Yilmaz et al., “Estimating Suspended Sediments Loads with Multi Variate Adaptive Regressions Splines, Teaching-Learning Based Optimizations, and the Artificial Bee Colony Model,” Sciences of the Total Environments, vol. 639, pp. 826-840, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Nouar AlDahoul et al., “Suspended Sediments Loads Predictions Using Long Shorts-Terms Memory Neural Networks,” Scientifics Report, vol. 11, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tim Berthold et al., “Seabed Sediments Classifications of Sides-Scans Sonar Data Using Convolution Neural Network,” 2017 IEEE Symposiums Series on Computational Intelligences (SSCI), Honolulu, USA, pp. 1-8, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Cheng-Chia Huang et al., “Real-Time Forecasting of Suspended Sediment Concentrations in Reservoirs by the Optimal Integration of Multiple Machine Learning Techniques,” Journals of Hydrology: Regionals Studies, vol. 34, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Aditi Awasthi, S.S. Lokhande, and M. Selva Balan, “Acoustics Signals Processing Techniques to Classify Reservoirs Sediment,” International Research Journal of Engineering and Technology, vol. 3, no. 6, pp. 2817-2819, 2016.
[Publisher Link]
[13] Xiaodong Cui et al., “Deep Learning Models for Seabed Sediments Classifications Based on Fuzzy Ranking Features Optimizations,” Marine Geology, vol. 432, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Issam Mohamed, and Imtiaz Shah, “Suspended Sediments Concentrations Modelling Using Conventional and Machines Learning Approach in Thames Rivers, London,” Journals of Water Managements Modelling, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] B. Bhattacharya, and D.P. Solomatine, “Machine Learning in Sedimentations Modeling,” Neural Network, vol. 19, no. 2, pp. 208-214, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Haoran Liu et al., “Sediments Identifications Using Machine Learning Classifier in a Mixed-Textures Dredge Pits of Louisiana Shelfs for Coastal Restorations,” Water, vol. 11, no. 6, pp. 1-18, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Senlin Zhu et al., “Machine Learning Approaches for Estimations of Sediments Settling Velocity,” Journal of Hydrology, vol. 586, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Maheswar Ojha, and Saumen Maiti, “Sediments Classifications Using Neural Network: An Example from the Sites-U1344A of IODP Expeditions 323 in the Bering Sea,” Deep Sea Research Part II: Topical Studies in Oceanography, vol. 125-126, pp. 202-213, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] P.J. Mitchell et al., “Sedimentations Rate in the Baltic Sea: A Machine Learning Approach,” Continental Shelfs Research, vol. 214, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ashish Mishra, and Santonu Goswami, “Analyzing Seasonal and Inter-Annual Turbidity of a Wetland Ecosystem in India Using Machine Learning and Time-Series Modeling,” Indian Space Research Organization, KIET Institutions, Research Report, 2022.
[Google Scholar] [Publisher Link]
[21] Xiaoming Qin et al., “Optimizing the Sediments Classifications of Small Side-Scans Sonar Image Based on Deep Learning,” IEEE Access, vol. 9, pp. 29416-29428, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] G. Revathy, P. Sathish Kumar, and Velayutham Rajendran, “Development of IDS Using Mining and Machine Learning Technique to Estimate DoS Malwares,” International Journal of Computational Sciences and Engineering, vol. 24, no. 3, pp. 259-275, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Viviana Siless et al., “A Comparison of Metric and Algorithm for Fiber Clustering,” 2013 International Workshops on Patterns Recognitions in Neuroimaging, Philadelphia, USA, pp. 190-193, 2013.
[CrossRef] [Google Scholar] [Publisher Link]