Optimizing Laboratory Investigations of Saline Intrusion by Incorporating Machine Learning Techniques

被引:10
|
作者
Etsias, Georgios [1 ]
Hamill, Gerard A. [1 ]
Benner, Eric M. [1 ]
Aguila, Jesus F. [1 ]
McDonnell, Mark C. [1 ]
Flynn, Raymond [1 ]
Ahmed, Ashraf A. [2 ]
机构
[1] Queens Univ Belfast, Sch Nat & Built Environm, Belfast BT9 5AG, Antrim, North Ireland
[2] Brunel Univ, Coll Engn Design & Phys Sci, London UB8 3PH, England
基金
英国工程与自然科学研究理事会;
关键词
saltwater intrusion; sandbox; artificial neural networks; image analysis; classification; regression; FRESH-WATER LENSES; AUTOMATED IMAGE-ANALYSIS; VARIABLE-DENSITY FLOW; SALTWATER INTRUSION; SEAWATER INTRUSION; COASTAL AQUIFERS; CONTAMINANT TRANSPORT; AGE-STRATIFICATION; REACTIVE TRANSPORT; GROUNDWATER-FLOW;
D O I
10.3390/w12112996
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Deriving saltwater concentrations from the light intensity values of dyed saline solutions is a long-established image processing practice in laboratory scale investigations of saline intrusion. The current paper presents a novel methodology that employs the predictive ability of machine learning algorithms in order to determine saltwater concentration fields. The proposed approach consists of three distinct parts, image pre-processing, porous medium classification (glass bead structure recognition) and saltwater field generation (regression). It minimizes the need for aquifer-specific calibrations, significantly shortening the experimental procedure by up to 50% of the time required. A series of typical saline intrusion experiments were conducted in homogeneous and heterogeneous aquifers, consisting of glass beads of varying sizes, to recreate the necessary laboratory data. An innovative method of distinguishing and filtering out the common experimental error introduced by both backlighting and the optical irregularities of the glass bead medium was formulated. This enabled the acquisition of quality predictions by classical, easy-to-use machine learning techniques, such as feedforward Artificial Neural Networks, using a limited amount of training data, proving the applicability of the procedure. The new process was benchmarked against a traditional regression algorithm. A series of variables were utilized to quantify the variance between the results generated by the two procedures. No compromise was found to the quality of the derived concentration fields and it was established that the proposed image processing technique is robust when applied to homogeneous and heterogeneous domains alike, outperforming the classical approach in all test cases. Moreover, the method minimized the impact of experimental errors introduced by small movements of the camera and the presence air bubbles trapped in the porous medium.
引用
收藏
页码:1 / 21
页数:21
相关论文
共 50 条
  • [11] A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques
    Singh G.
    Khare N.
    International Journal of Computers and Applications, 2022, 44 (07) : 659 - 669
  • [12] Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques
    Musthafa, Muhammad Bisri
    Huda, Samsul
    Kodera, Yuta
    Ali, Md. Arshad
    Araki, Shunsuke
    Mwaura, Jedidah
    Nogami, Yasuyuki
    SENSORS, 2024, 24 (13)
  • [13] Public Domain Datasets for Optimizing Network Intrusion and Machine Learning Approaches
    Deraman, Maznan
    Desa, Abd Jalil
    Othman, Zulaiha Ali
    2011 3RD CONFERENCE ON DATA MINING AND OPTIMIZATION (DMO), 2011, : 51 - 56
  • [14] Security intrusion detection using quantum machine learning techniques
    Kalinin, Maxim
    Krundyshev, Vasiliy
    JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES, 2023, 19 (01) : 125 - 136
  • [15] Supervised Machine Learning Techniques for Efficient Network Intrusion Detection
    Aboueata, Nada
    Alrasbi, Sara
    Erbad, Aiman
    Kassler, Andreas
    Bhamare, Deval
    2019 28TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN), 2019,
  • [16] Security for the Metaverse: Blockchain and Machine Learning Techniques for Intrusion Detection
    Truong, Vu Tuan
    Le, Long Bao
    IEEE NETWORK, 2024, 38 (05): : 204 - 212
  • [17] Analysis on intrusion detection system using machine learning techniques
    Seraphim B.I.
    Poovammal E.
    Lecture Notes on Data Engineering and Communications Technologies, 2021, 66 : 423 - 441
  • [18] Intrusion detection based on behavior mining and machine learning techniques
    Mukkamala, Srinivas
    Xu, Dennis
    Sung, Andrew H.
    ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4031 : 619 - 628
  • [19] A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
    Vitorino, Joao
    Andrade, Rui
    Praca, Isabel
    Sousa, Orlando
    Maia, Eva
    FOUNDATIONS AND PRACTICE OF SECURITY, FPS 2021, 2022, 13291 : 191 - 207
  • [20] Analysis of Machine Learning Techniques Based Intrusion Detection Systems
    Sharma, Rupam Kr.
    Kalita, Hemanta Kumar
    Borah, Parashjyoti
    PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING, NETWORKING AND INFORMATICS, ICACNI 2015, VOL 2, 2016, 44 : 485 - 493