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Algorithms
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Development of Machine Learning and Artificial Intelligence Algorithms in Environmental...
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Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 7248

Special Issue Editors

Dr. Hugo Wai Leung Mak

E-Mail Website
Guest Editor
1. Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
2. Department of Mathematics, The Chinese University of Hong Kong, Hong Kong SAR, China
Interests: applied and computational mathematics; image and data analytics; machine learning algorithms; remote sensing; numerical modeling; smart city development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesc Pozo

E-Mail Website
Guest Editor
Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of machine learning and artificial intelligence technologies in the current era of big data, scientists can acquire a better understanding of our surrounding environment by synergizing different datasets and using properly trained and validated algorithms, for example, satellite imageries and datasets, local and urban monitoring networks, fine-scale emission inventories, meteorological and atmospheric attributes, numerical modeling, and post-processed outputs. Measurements obtained from low-cost sensors and raw observational datasets can also be integrated into the model development process to fine-tune specific dependent parameters of the entire algorithmic framework, thus enhancing the validity and reliability of the developed algorithms. This is particularly useful when attempting to conduct large-scale spatial and temporal assessments, as well as associating relevant predicted results to enhance health qualities and implement relevant policies. Further, insights obtained from the algorithmic development process do not have any geographical limits, and the appropriate combination of various models with the latest data analysis tools has proved to return better retrieval results in the long run. Therefore, it is of particular interest to explore how digital advancement could gradually lead to more effective and systematic environmental retrieval and monitoring.

This Special Issue seeks to publish and promote new and innovate ideas in the development of trustworthy algorithmic frameworks for the purpose of environmental monitoring and environmental data analysis, as well as the application of these frameworks in practice to conduct large-scale trend analyses and assessments. Original research articles and literature reviews of relevant topics are highly welcome, contributing to a joint effort to steer technological advancement forward, and as a result create a sustainable world in the foreseeable future.

Dr. Hugo Wai Leung Mak
Dr. Francesc Pozo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning/machine learning algorithms
  • environmental informatics and analyses
  • algorithmic design in environmental retrieval
  • atmospheric monitoring and assessment
  • land use monitoring and assessment
  • traffic monitoring and assessment
  • large-scale spatial and temporal environmental dynamics
  • data assimilation/fusion in large-scale model development
  • artificial intelligence and big data analytics
  • microsensor technology in environmental model development

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Published Papers (7 papers)

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Research

26 pages, 3766 KB  
Article
Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
by Amar Lokman, Wan Zakiah Wan Ismail and Nor Azlina Ab Aziz
Algorithms 2025, 18(8), 494; https://doi.org/10.3390/a18080494 - 8 Aug 2025
Viewed by 365
Abstract
Water quality assessment plays a vital role in environmental monitoring and resource management. This study aims to enhance the predictive modeling of the Water Quality Index (WQI) using a combination of statistical diagnostics and machine learning techniques. Data collected from six river locations [...] Read more.
Water quality assessment plays a vital role in environmental monitoring and resource management. This study aims to enhance the predictive modeling of the Water Quality Index (WQI) using a combination of statistical diagnostics and machine learning techniques. Data collected from six river locations in Malaysia are analyzed. The methodology involves collecting water quality data from six river locations in Malaysia, followed by a series of statistical analyses including assumption testing (shapiro–wilk and breusch–pagan tests), diagnostic evaluations, feature importance analysis, and principal component analysis (PCA). Decision tree regression (DTR) and autoregressive integrated moving average (ARIMA) are employed for regression, while random forest is used for classification. Learning curve analysis is conducted to evaluate model performance and generalization. The results indicate that dissolved oxygen (DO) and ammoniacal nitrogen (AN) are the most influential parameters, with normalized importance scores of 1.000 and 0.565, respectively. The breusch–pagan test identifies significant heteroscedasticity (p-value = (3.138e−115)), while the Shapiro–Wilk test confirms non-normality (p-value = 0.0). PCA effectively reduces dimensionality while preserving 95% of dataset variance, optimizing computational efficiency. Among the regression models, ARIMA demonstrates better predictive accuracy than DTR. Meanwhile, random forest achieves high classification performance and shows strong generalization capability with increasing training data. Learning curve analysis reveals overfitting in the regression model, suggesting the need for hyperparameter tuning, while the classification model demonstrates improved generalization with additional training data. Strong correlations among key parameters indicate potential multicollinearity, emphasizing the need for careful feature selection. These findings highlight the synergy between statistical pre-processing and machine learning, offering a more accurate and efficient approach to water quality prediction for informed environmental policy and real-time monitoring systems. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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23 pages, 5245 KB  
Article
Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
by Dandan Li, Shaojun Zheng, Chenyu Zheng, Lingling Xie and Li Yan
Algorithms 2025, 18(7), 431; https://doi.org/10.3390/a18070431 - 14 Jul 2025
Viewed by 335
Abstract
The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity [...] Read more.
The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R2) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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26 pages, 510 KB  
Article
Integrating Feature Selection and Deep Learning: A Hybrid Approach for Smart Agriculture Applications
by Ali Roman, Md Mostafizer Rahman, Sajjad Ali Haider, Tallha Akram and Syed Rameez Naqvi
Algorithms 2025, 18(4), 222; https://doi.org/10.3390/a18040222 - 12 Apr 2025
Cited by 1 | Viewed by 840
Abstract
This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization [...] Read more.
This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization algorithms, leverage biological behaviors for feature selection, they are limited by their inability to synergize diverse strategies, resulting in suboptimal performance and scalability. To address this, we introduce the Hybrid Predator Algorithm for Classification (HPA-C), a novel hybrid feature selection algorithm that uniquely integrates the framework of a nature-inspired feature selection technique with position update equations from other algorithms, harnessing diverse biological behaviors like echolocation, foraging, and collaborative hunting. Coupled with a custom convolutional neural network (CNN), HPA-C achieves superior classification accuracy (98.6–99.8%) on agricultural datasets (Plant Leaf Diseases, Weed Detection, Fruits-360, and Fresh n Rotten) and demonstrates exceptional adaptability across diverse imagery applications. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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14 pages, 738 KB  
Article
A Hybrid Dimensionality Reduction Procedure Integrating Clustering with KNN-Based Feature Selection for Unsupervised Data
by David Gutman, Nir Perel, Oana Bărbulescu and Oded Koren
Algorithms 2025, 18(4), 188; https://doi.org/10.3390/a18040188 - 26 Mar 2025
Cited by 1 | Viewed by 1202
Abstract
This paper proposes a novel hybrid approach that combines unsupervised feature extraction through clustering and unsupervised feature selection for data reduction, specifically targeting high-dimensional data. The proposed method employs K-means clustering for feature extraction, where cluster membership serves as a new feature representation, [...] Read more.
This paper proposes a novel hybrid approach that combines unsupervised feature extraction through clustering and unsupervised feature selection for data reduction, specifically targeting high-dimensional data. The proposed method employs K-means clustering for feature extraction, where cluster membership serves as a new feature representation, capturing the inherent data characteristics. Subsequently, the K-Nearest Neighbors (KNN) and Random Forest algorithms are utilized for supervised feature selection, identifying the most relevant feature to enhance model performance. This hybrid approach leverages the strengths of both unsupervised and supervised learning techniques. The new algorithm was applied to 13 different tabular datasets, with 9 datasets showing significant improvements across various performance metrics (accuracy, precision, recall, and F1-score) in both KNN and Random Forest models, despite substantial feature reduction. In the remaining four datasets, we achieved substantial dimensionality reduction with only negligible performance decreases. This improvement in performance while reducing dimensionality highlights the potential of the proposed method within the procedure, where datasets are treated without prior knowledge or assumptions. The proposed method offers a promising solution for handling high-dimensional data, enhancing model performance while maintaining interpretability and ease of integration within the proposed frameworks, with the ability to be irrespective of supervised or unsupervised designation datasets while reducing the dependency on a target or label features. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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35 pages, 9522 KB  
Article
Decoding PM2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM
by Minru Chen, Binglin Liu, Mingzhi Liang and Nini Yao
Algorithms 2025, 18(3), 167; https://doi.org/10.3390/a18030167 - 14 Mar 2025
Cited by 1 | Viewed by 785
Abstract
With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction of PM2.5 concentration is of great significance to environmental protection and public health. Our study takes Nanning urban area, which has unique geographical, climatic and pollution [...] Read more.
With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction of PM2.5 concentration is of great significance to environmental protection and public health. Our study takes Nanning urban area, which has unique geographical, climatic and pollution source characteristics, as the object. Based on the dual-time resolution raster data of the China High-resolution and High-quality PM2.5 Dataset (CHAP) from 2012 to 2023, the PM2.5 concentration prediction study is carried out using SARIMA, Prophet and LightGBM models. The study systematically compares the performance of each model from the spatial and temporal dimensions using indicators such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). The results show that the LightGBM model has a strong ability to mine complex nonlinear relationships, but its stability is poor. The Prophet model has obvious advantages in dealing with seasonality and trend of time series, but it lacks adaptability to complex changes. The SARIMA model is based on time series prediction theory and performs well in some scenarios, but has limitations in dealing with non-stationary data and spatial heterogeneity. Our research provides a multi-dimensional model performance reference for subsequent PM2.5 concentration predictions, helps researchers select models reasonably according to different scenarios and needs, provides new ideas for analyzing concentration change patterns, and promotes the development of related research in the field of environmental science. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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21 pages, 2229 KB  
Article
Multi-Server Two-Way Communication Retrial Queue Subject to Disaster and Synchronous Working Vacation
by Tzu-Hsin Liu, He-Yao Hsu and Fu-Min Chang
Algorithms 2025, 18(1), 24; https://doi.org/10.3390/a18010024 - 5 Jan 2025
Cited by 1 | Viewed by 953
Abstract
This research analyzes a multi-server retrial queue with two types of calls: working vacation and working breakdown. The incoming call may enter the retrial queue and attempt to seize a server after a random delay if all the servers are unavailable upon arrival. [...] Read more.
This research analyzes a multi-server retrial queue with two types of calls: working vacation and working breakdown. The incoming call may enter the retrial queue and attempt to seize a server after a random delay if all the servers are unavailable upon arrival. In its idle time, the server makes outgoing calls. All the servers take a synchronous working vacation when the system empties after regular service. The system may fail at any time due to disasters, forcing all the calls within the service area to leave the system and causing all the main servers to fail. When the main servers fail, the repair process begins immediately. The standby servers serve arriving customers at a lower level of service during the working breakdown or working vacation. For this model, we derive an explicit expression for the stationary distribution with the help of the quasi-birth-and-death process and the matrix geometric method. Further, the formulas of various system performance indices are developed. An application example is given and several numerical experiments are performed to verify the analytical results. We also perform the comparative analysis of retrial queues with/without two-way communication and two-way communication retrial queues with/without disasters. The results reveal that the proper consideration of outgoing calls to the server can reduce the average time spent in the buffer. Furthermore, a more reliable server reduces the server idle rate. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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17 pages, 29257 KB  
Article
Realistic Simulation of Dissolution Process on Rock Surface
by Xiaoying Nie, Chunqing Zhou, Zhaoxi Yu and Gang Yang
Algorithms 2024, 17(10), 466; https://doi.org/10.3390/a17100466 - 19 Oct 2024
Viewed by 1473
Abstract
Hydraulic dissolution, driven by carbon dioxide-rich precipitation and runoff, leads to the gradual breakdown and removal of soluble rock materials, creating unique surface and subsurface features. Dissolution is a complex process that is related to numerous factors, and the complete simulation of its [...] Read more.
Hydraulic dissolution, driven by carbon dioxide-rich precipitation and runoff, leads to the gradual breakdown and removal of soluble rock materials, creating unique surface and subsurface features. Dissolution is a complex process that is related to numerous factors, and the complete simulation of its process is a challenging problem. On the basis of deep investigation of the theories of geology and rock geomorphology, this paper puts forward a method for simulating the dissolution phenomenon on a rock surface. Around the movement of water, this method carries out dissolution calculations, including processes such as droplet dissolution, water flow, dissolution, deposition, and evaporation. It also considers the lateral dissolution effect of centrifugal force when water flows through bends, achieving a comprehensive simulation of the dissolution process. This method can realistically simulate various typical karst landforms such as karst pits, karst ditches, and stone forests, with interactive simulation efficiency. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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