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Water Modeling Using Combined Machine Learning and Fieldwork Investigation
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Water Modeling Using Combined Machine Learning and Fieldwork Investigation

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2328

Special Issue Editors

Dr. Joonghyeok Heo

E-Mail Website
Guest Editor
Department of Geosciences, University of Texas Permian Basin, Odessa, TX 79762, USA
Interests: environmental science; energy sustainability; geology and geophysics; machine learning; modeling investigation; field works; geoinformatics
Dr. Huidae Cho

E-Mail Website
Guest Editor
Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: smart infrastructure; water resources engineering; hydrologic modeling; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Netra Regmi

E-Mail Website
Guest Editor
Oklahoma Geological Survey, University of Oklahoma, Norman, OK 73019, USA
Interests: environmental hazards; machine learning application; earth resources engineering; GIS; soil-landscape modeling

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the intersection of computational modeling and advanced machine learning techniques in the context of hydrological systems. The primary focus is to highlight innovative approaches where machine learning is applied to enhance predictive models of water dynamics, optimize water resource management, and improve our understanding of environmental water systems. Submissions are invited that cover a range of topics, including, but not limited to, predictive modeling of water quality and quantity, flood forecasting, groundwater monitoring, and the integration of real-time data analytics in hydrological models.

This Special Issue contributes to the growing body of literature that seeks to bridge traditional water modeling approaches with cutting-edge machine learning methods. By situating itself at the convergence of these fields, it seeks to advance research in areas where machine learning can offer novel insights into complex water-related challenges. Existing research has focused on the use of either water modeling or machine learning independently; however, this issue calls for contributions that demonstrate the synergistic potential when both are combined to address multifaceted problems in water science and management.

Dr. Joonghyeok Heo
Dr. Huidae Cho
Dr. Netra Regmi
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. Water is an international peer-reviewed open access semimonthly 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 2600 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

  • machine learning
  • field survey
  • water modeling
  • big data analysis
  • geostatistic analysis
  • water quality and water quantity
  • environmental sustainability

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Published Papers (5 papers)

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Research

18 pages, 5178 KiB  
Article
Quantification of Suspended Sediment Concentration Using Laboratory Experimental Data and Machine Learning Model
by Sathvik Reddy Nookala, Jennifer G. Duan, Kun Qi, Jason Pacheco and Sen He
Water 2025, 17(15), 2301; https://doi.org/10.3390/w17152301 (registering DOI) - 2 Aug 2025
Abstract
Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images [...] Read more.
Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article
(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)
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18 pages, 11666 KiB  
Article
A Hybrid XAJ-LSTM-TFM Model for Improved Runoff Simulation in the Poyang Lake Basin: Integrating Physical Processes with Temporal and Lag Feature Learning
by Haoyu Jiang and Chunxiao Zhang
Water 2025, 17(14), 2146; https://doi.org/10.3390/w17142146 - 18 Jul 2025
Viewed by 303
Abstract
As the largest freshwater lake in China, Poyang Lake plays a crucial role in hydrological processes. Conventional models often fail to capture the time-lagged relationships between meteorological drivers and runoff responses, while lacking regional generalization capability. To address these limitations, this study proposes [...] Read more.
As the largest freshwater lake in China, Poyang Lake plays a crucial role in hydrological processes. Conventional models often fail to capture the time-lagged relationships between meteorological drivers and runoff responses, while lacking regional generalization capability. To address these limitations, this study proposes a novel XAJ-LSTM-TFM hybrid model that accounts for time-lagged hydrological responses and enhances the regional applicability of the Xinanjiang model. The model innovatively integrates the physical mechanisms of the Xinanjiang model with the temporal learning capacity of LSTM networks. By incorporating intermediate hydrological variables (including interflow and groundwater flow) along with 1–3 day lagged meteorological features, the model achieves an average 15.3% improvement in Nash–Sutcliffe Efficiency (NSE) across five sub-basins, with the Ganjiang Basin attaining an NSE of 0.812 and a 25.7% reduction in flood peak errors. The results demonstrate superior runoff simulation performance and reliable generalization capability under intensive anthropogenic activities. Full article
(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)
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23 pages, 8986 KiB  
Article
Water Flow Forecasting Model Based on Bidirectional Long- and Short-Term Memory and Attention Mechanism
by Xinfeng Zhao, Shengwen Dong, Hui Rao and Wuyi Ming
Water 2025, 17(14), 2118; https://doi.org/10.3390/w17142118 - 16 Jul 2025
Viewed by 371
Abstract
Accurate forecasting of river water flow helps to warn of floods and droughts in advance, provides a basis for the rational allocation of water resources, and at the same time, offers important support for the safe operation of hydropower stations and water conservancy [...] Read more.
Accurate forecasting of river water flow helps to warn of floods and droughts in advance, provides a basis for the rational allocation of water resources, and at the same time, offers important support for the safe operation of hydropower stations and water conservancy projects. Water flow is characterized by time series, but the existing models focus on the positive series when LSTM is applied, without considering the different contributions of the water flow series to the model at different moments. In order to solve this problem, this study proposes a river water flow prediction model, named AT-BiLSTM, which mainly consists of a bidirectional layer and an attention layer. The bidirectional layer is able to better capture the long-distance dependencies in the sequential data by combining the forward and backward information processing capabilities. In addition, the attention layer focuses on key parts and ignores irrelevant information when processing water flow data series. The effectiveness of the proposed method was validated against an actual dataset from the Shizuishan monitoring station on the Yellow River in China. The results confirmed that compared with the RNN model, the proposed model significantly reduced the MAE, MSE, and RMSE on the dataset by 27.16%, 42.01%, and 23.85%, respectively, providing the best predictive performance among the six compared models. Moreover, this attention mechanism enables the model to show good performance in 72 h (3 days) forecast, keeping the average prediction error below 6%. This implies that the proposed hybrid model could provide a decision base for river flow flood control and resource allocation. Full article
(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)
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17 pages, 5319 KiB  
Article
Quantitative Detection of Floating Debris in Inland Reservoirs Using Sentinel-1 SAR Imagery: A Case Study of Daecheong Reservoir
by Sunmin Lee, Bongseok Jeong, Donghyeon Yoon, Jinhee Lee, Jeongho Lee, Joonghyeok Heo and Moung-Jin Lee
Water 2025, 17(13), 1941; https://doi.org/10.3390/w17131941 - 28 Jun 2025
Viewed by 373
Abstract
Rapid rises in water levels due to heavy rainfall can lead to the accumulation of floating debris, posing significant challenges for both water quality and resource management. However, real-time monitoring of floating debris remains difficult due to the discrepancy between meteorological conditions and [...] Read more.
Rapid rises in water levels due to heavy rainfall can lead to the accumulation of floating debris, posing significant challenges for both water quality and resource management. However, real-time monitoring of floating debris remains difficult due to the discrepancy between meteorological conditions and the timing of debris accumulation. To address this limitation, this study proposes an amplitude change detection (ACD) model based on time-series synthetic aperture radar (SAR) imagery, which is less affected by weather conditions. The model statistically distinguishes floating debris from open water based on their differing scattering characteristics. The ACD approach was applied to 18 pairs of Sentinel-1 SAR images acquired over Daecheong Reservoir from June to September 2024. A stringent type I error threshold (α < 1 × 10−8) was employed to ensure reliable detection. The results revealed a distinct cumulative effect, whereby the detected debris area increased immediately following rainfall events. A positive correlation was observed between 10-day cumulative precipitation and the debris-covered area. For instance, on 12 July, a floating debris area of 0.3828 km2 was detected, which subsequently expanded to 0.4504 km2 by 24 July. In contrast, on 22 August, when rainfall was negligible, no debris was detected (0 km2), indicating that precipitation was a key factor influencing the detection sensitivity. Comparative analysis with optical imagery further confirmed that floating debris tended to accumulate near artificial barriers and narrow channel regions. Overall, this study demonstrates that this spatial pattern suggests the potential to use detection results to estimate debris transport pathways and inform retrieval strategies. Full article
(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)
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18 pages, 4380 KiB  
Article
Deep Learning-Based Retrieval of Chlorophyll-a in Lakes Using Sentinel-1 and Sentinel-2 Satellite Imagery
by Bongseok Jeong, Sunmin Lee, Joonghyeok Heo, Jeongho Lee and Moung-Jin Lee
Water 2025, 17(11), 1718; https://doi.org/10.3390/w17111718 - 5 Jun 2025
Viewed by 757
Abstract
Remote sensing and AI models have been utilized for monitoring Chlorophyll-a (Chl-a), a primary indicator of eutrophication across broad water bodies. Previous studies have primarily relied on optical remote sensing data for assessing Chl-a’s spectral characteristics. Synthetic-aperture radar (SAR) data, which contain valuable [...] Read more.
Remote sensing and AI models have been utilized for monitoring Chlorophyll-a (Chl-a), a primary indicator of eutrophication across broad water bodies. Previous studies have primarily relied on optical remote sensing data for assessing Chl-a’s spectral characteristics. Synthetic-aperture radar (SAR) data, which contain valuable information about surface algae containing Chl-a, remains underutilized despite its high potential for improving Chl-a retrieval accuracy. Therefore, this study aims to develop a Convolutional neural network (CNN) based Chl-a retrieval model utilizing both SAR data and optical data in Korean lakes. The model dataset was established by acquiring Chl-a concentration data and Sentinel-1/2 imagery from the Copernicus Open Access Hub. The CNN model trained on both optical and SAR data exhibited superior performance (R2 = 0.7992, RMSE = 10.3282 mg/m3, RPD = 2.2315) compared with the model trained exclusively on optical data. Moreover, SAR data exhibited moderate variable importance among all variables, demonstrating their efficacy as input variables for Chl-a concentration estimation. Furthermore, the CNN model estimated Chl-a concentrations with a spatial distribution that matched the observed spatial heterogeneity of Chl-a concentrations. These results are expected to serve as a foundation for future research on remote monitoring of Chl-a using such data. Full article
(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)
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