Article
Mathematics
Ali Najem Alkawaz, Jeevan Kanesan, Irfan Anjum Badruddin, Sarfaraz Kamangar, Mohamed Hussien, Maughal Ahmed Ali Baig, N. Ameer Ahammad
Summary: This study presents two models of self-organizing map (SOM) formulated as an optimal control problem. The first model focuses on the weight updating equation of the best matching units in each iteration, while the second model considers the weight updating equation of all nodes in the SOM. The SOMOC2 model performs better by considering all nodes in the Hamiltonian equation and produces a greater improvement in terms of mean quantization error.
Article
Environmental Sciences
A. T. M. Sakiur Rahman, Yumiko Kono, Takahiro Hosono
Summary: A holistic understanding of hydrochemical features is essential for water resources management and protection. Machine learning techniques like self-organizing map (SOM) can provide more detailed information on hydrochemical processes than traditional approaches, particularly in complex regions. Proper application of SOM is crucial for accurate results.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Engineering, Civil
Li-Chiu Chang, Wu-Han Wang, Fi-John Chang
Summary: The study compared the effectiveness of two SOM training strategies, with the S2 strategy demonstrating higher efficiency and effectiveness in constructing regional flood inundation maps.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Chung-Mo Lee, Hanna Choi, Yongcheol Kim, MoonSu Kim, HyunKoo Kim, Se-Yeong Hamm
Summary: This study investigated the influence of land use types on nitrate-nitrogen contamination in groundwater in a typical rural area in South Korea. The findings showed that nitrate-nitrogen mainly originated from public facilities and livestock areas, with different land use types having distinct recharge routes for nitrate-nitrogen into the groundwater system. The shallow groundwaters in the study area were classified into three clusters based on their chemical constituents and land-use properties using SOM, PCA, and HCA.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Computer Science, Artificial Intelligence
Mahsa Hasanpour Kashani, Mohammad Reza Nikpour, Reza Jalali
Summary: This research compares the capabilities of four data-driven models in predicting water quality parameters of groundwater in Ardabil Province, Iran. The results show that these models perform better in simulating SAR data than the traditional multiple linear regression model.
Article
Engineering, Environmental
Emmanouil A. Varouchakis, Dimitri Solomatine, Gerald A. Corzo Perez, Seifeddine Jomaa, George P. Karatzas
Summary: Successful modelling of groundwater level variations in complex aquifer systems requires integration of geostatistics and machine learning approaches. This study focuses on cases with large and randomly distributed datasets in different aquifer types. Self-Organizing Maps are used to identify locally similar data inputs and substitute the uncertain correlation length of the variogram model. Transgaussian Kriging is then applied to estimate the bias-corrected spatial distribution of groundwater level. The proposed methodology shows a significant improvement compared to classical geostatistical approaches.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Water Resources
Khairul Hasan, Sondipon Paul, Tareq Jamal Chy, Anzhelika Antipova
Summary: This study focused on the trends of groundwater level changes in the Sylhet region of Bangladesh by using geostatistical methods to analyze the annual-average depth to the water table at 46 observation wells. The results showed a substantial increase in groundwater depths in some locations from 2000 to 2015, identifying vulnerable zones in the area due to lowering groundwater trend. This research contributes to groundwater management in developing countries by providing insights into spatial and temporal groundwater variation.
APPLIED WATER SCIENCE
(2021)
Review
Food Science & Technology
Diego Galvan, Luciane Effting, Luiz Torres Neto, Carlos Adam Conte-Junior
Summary: Essential oils are important commercial products with antioxidant and antimicrobial properties. A systematic review and meta-analysis using chemometric tools were conducted to analyze patterns of these activities based on geographic, botanical, chemical, and microbiological distribution. The study revealed key elements such as Brazil, Asia, Thymus genus, Thymus vulgaris species, Lamiaceae family, limonene, and Escherichia coli and Candida albicans species as prominent factors in assessing the antioxidant and antimicrobial activities of essential oils.
COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY
(2021)
Article
Environmental Sciences
Shaojie Lv, Zongwen Zhang, Ning Sun, Zheming Shi, Jia Li, Shen Qu
Summary: Groundwater quality assessment is crucial for understanding the suitability of groundwater resources. This study proposed a method combining self-organizing map and entropy-based weight determining method to assess groundwater quality. The study classified sampling points into five clusters, indicating different sources of contamination. The results showed that groundwater affected by domestic sewage discharge sources had better quality compared to other sources in the study area.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Agriculture, Multidisciplinary
Navsal Kumar, Rabee Rustum, Vijay Shankar, Adebayo J. Adeloye
Summary: The study developed a SOM-based model to predict CWSI values using microclimatic variables and successfully evaluated its performance for different irrigation levels. The model performed well, especially in the CWSI range between 0.1 and 0.6, which is commonly observed in irrigation scheduling for field crops.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Automation & Control Systems
Dominik Olszewski
Summary: The study introduces an enhanced adaptive version of SOM that preserves input data structure and captures data scattering, which has been empirically proven to be superior to other data visualization techniques.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Civil
V Gholami, M. R. Khaleghi, S. Pirasteh, Martijn J. Booij
Summary: The study utilized geospatial artificial intelligence methods to simulate groundwater quality in the Mazandaran plain in Iran and compared the performance of three methods, with CANFIS demonstrating the highest performance in the test stage. The results can be used for managing groundwater quality and contributing to sustainable development goals.
WATER RESOURCES MANAGEMENT
(2022)
Article
Geosciences, Multidisciplinary
Flora Giudicepietro, Antonietta M. Esposito, Laura Spina, Andrea Cannata, Daniele Morgavi, Lukas Layer, Giovanni Macedonio
Summary: The study focuses on using an unsupervised neural network, Self-Organizing Map (SOM), to cluster seismo-acoustic events and investigate the cause-effect relationships between signals and processes. The experimental conditions were controlled to validate the effectiveness of the neural network in clustering events.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Geochemistry & Geophysics
Ndiye M. Kebonye, Peter N. Eze, Kingsley John, Asa Gholizadeh, Julie Dajcl, Ondrej Drabek, Karel Nemecek, Lubos Boruvka
Summary: The study demonstrates the application of SGS and SeOM-ANN in heavily polluted mining floodplain soils in Europe, providing a powerful visualization tool for categorizing PTE concentration levels.
JOURNAL OF GEOCHEMICAL EXPLORATION
(2021)
Article
Health Care Sciences & Services
Agata Ossowska, Aida Kusiak, Dariusz Swietlik
Summary: This study used self-organizing maps (SOM) to group the clinical profiles of patients with periodontitis, revealing three distinct clusters based on the severity and stage of the disease. The analysis showed significant differences in key indicators among the groups. Self-organizing maps provide a clearer understanding of periodontitis progression compared to conventional statistics.
JOURNAL OF PERSONALIZED MEDICINE
(2023)