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Application of Machine Learning in Hydrological Monitoring
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Application of Machine Learning in Hydrological Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 January 2026) | Viewed by 4142

Special Issue Editors

Dr. Yusuf Sermet

E-Mail Website
Guest Editor
IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA 52246-1585, USA
Interests: artificial intelligence; web technologies; augmented and virtual reality; hydroinformatics; large language models
Dr. Zhouyayan Li

E-Mail Website
Guest Editor Assistant
Civil and Environmental Engineering, University of Iowa, Iowa City, IA, USA
Interests: floods; geographical information systems (GIS); remote sensing; artificial intelligence; hydorinformatics

Special Issue Information

Dear Colleagues,

The integration of machine learning, particularly deep learning, in hydrological monitoring has significantly transformed its efficiency and capability of analyzing and predicting water-related phenomena. This Special Issue, titled "Application of Machine Learning in Hydrological Monitoring", seeks to explore innovative machine learning methodologies specifically tailored for enhancing hydrological data analysis and decision-making processes. Contributions to this Issue will focus on novel machine learning techniques that improve the accuracy of hydrological predictions, optimize data retrieval and management, and enhance disaster analytics and decision support systems. We invite research that utilizes AI-driven models for tasks such as flood forecasting, water quality monitoring, streamflow prediction, and rainfall data analysis. Papers may also discuss advancements in data augmentation techniques, including the integration of multi-source data for comprehensive flood modeling and predictions, as well as conversational AI approaches using large language models (LLMs). By bringing together these focused studies, the Special Issue aims to highlight the transformative impact of machine learning in hydrology and encourage further interdisciplinary collaborations to advance water resource management.

Dr. Yusuf Sermet
Guest Editor

Dr. Zhouyayan Li
Guest Editor Assistant

Manuscript Submission Information

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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
  • hydrological monitoring
  • AI-driven models
  • data augmentation
  • flood forecasting
  • water quality analysis
  • decision support systems
  • streamflow prediction
  • data retrieval techniques
  • large language models

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Published Papers (3 papers)

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Research

14 pages, 3747 KB  
Article
Assessing the Ability of the Variable Length Block Bootstrapping Model for the Generation of Multiple Stochastic Hydrometric Data Types
by Rachel Makungo and John Ndiritu
Water 2026, 18(9), 1023; https://doi.org/10.3390/w18091023 - 25 Apr 2026
Viewed by 626
Abstract
Stochastic inputs are essential for incorporating hydrological variability in water resources assessment, planning, and management. However, most studies focus on the generation of precipitation and temperature, precipitation and streamflow, and precipitation and evaporation, with limited incorporation of groundwater levels. This study assessed the [...] Read more.
Stochastic inputs are essential for incorporating hydrological variability in water resources assessment, planning, and management. However, most studies focus on the generation of precipitation and temperature, precipitation and streamflow, and precipitation and evaporation, with limited incorporation of groundwater levels. This study assessed the ability of the Variable Length Block (VLB) bootstrapping model for simultaneously generating stochastic sequences of rainfall, evaporation, and groundwater levels. The performance of the model was assessed by comparing single statistics of historical time series located within the box plots of 100 annual and monthly stochastically generated time series. The model preserved eight of the nine statistics adequately, except for skewness, across all variables, with historical values for evaporation and groundwater levels falling below and above the interquartile range for 12 months. All the historic statistics for rainfall, evaporation, and groundwater levels were within the interquartile ranges of the box plots for 83, 71, and 71% of the time, respectively. The historic statistics for rainfall, evaporation, and groundwater levels were within the box plot ranges for 100, 98, and 99% of the time, respectively. These findings indicated reasonably successful generation, and the VLB generator was therefore considered applicable for the stochastic generation of multiple hydrometric data types. Full article
(This article belongs to the Special Issue Application of Machine Learning in Hydrological Monitoring)
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23 pages, 3889 KB  
Article
Enhanced Runoff Prediction in Zijiang River Basin Using Machine Learning and SHAP-Based Interpretability
by Kaiwen Ma, Changbo Jiang, Yuannan Long, Zhiyuan Wu and Shixiong Yan
Water 2026, 18(5), 601; https://doi.org/10.3390/w18050601 - 2 Mar 2026
Viewed by 646
Abstract
To address the limitations of traditional runoff prediction methods—namely, the oversimplification of meteorological factor selection, ambiguous interactions among core variables, and the disruptive influence of redundant inputs—this study focuses on the Zijiang River Basin as a representative case. A suite of machine learning [...] Read more.
To address the limitations of traditional runoff prediction methods—namely, the oversimplification of meteorological factor selection, ambiguous interactions among core variables, and the disruptive influence of redundant inputs—this study focuses on the Zijiang River Basin as a representative case. A suite of machine learning models, including Long Short-Term Memory Neural Network (LSTM), Convolutional Neural Network (CNN)-LSTM, Temporal Convolutional Network (TCN), and Gradient Boosting Regression Tree (GBRT), was constructed and trained using 13 distinct combinations of meteorological variables. These configurations were systematically evaluated to assess their compatibility with each model in simulating daily runoff patterns. Additionally, the Shapley Additive Explanations (SHAP) algorithm was employed to quantitatively assess the contribution of each factor to predictive accuracy. Among the models tested, the TCN model consistently demonstrated superior performance, particularly in mitigating the effects of irrelevant or redundant features. The GBRT model showed distinctive strengths in accurately predicting peak flow timings. Of all input configurations, the combination of “runoff + precipitation + evaporation + temperature” emerged as the most effective. Findings indicate that the predictive value of individual meteorological variables hinges primarily on their direct correlation with runoff, while the effectiveness of multi-factor schemes depends on the degree of functional integration—specifically, the coupling of hydrological recharge, consumption, and regulatory processes. The presence of redundant variables was found to impair model performance unless they contributed to a meaningful synergistic relationship with core inputs. The SHAP analysis further reinforced these insights: precipitation-related variables proved to be the most critical to prediction accuracy, whereas temperature and evaporation served more complementary roles. Notably, the inclusion of relative humidity tended to suppress runoff responses and increased deviation in peak timing estimates. These findings shed light on the nuanced interplay between meteorological input design and model selection, offering a robust foundation for optimizing data-driven runoff prediction frameworks. Full article
(This article belongs to the Special Issue Application of Machine Learning in Hydrological Monitoring)
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31 pages, 5616 KB  
Article
Deep Signals: Enhancing Bottom Temperature Predictions in Norway’s Mjøsa Lake Through VMD- and EMD-Boosted Machine Learning Models
by Sertac Oruc, Mehmet Ali Hınıs, Zeliha Selek and Türker Tuğrul
Water 2025, 17(18), 2673; https://doi.org/10.3390/w17182673 - 10 Sep 2025
Cited by 6 | Viewed by 1880
Abstract
In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improve forecast accuracy using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest [...] Read more.
In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improve forecast accuracy using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) with the monthly average data of Mjøsa, the largest lake in Norway, between 1950 and 2024 from the ERA5-Land FLake model. A total of 70% of the dataset was used for training and 30% was reserved for testing. To assess the performance several metrics, correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Performance Index (PI), RMSE-based RSR, and Root Mean Square Error (RMSE) were used. The results revealed that without decomposition, the GPR-M03 combination outperforms other models (with scores r = 0.9662, NSE = 0.9186, KGE = 0.8786, PI = 0.0231, RSR = 0.2848, and RMSE = 0.2000). Considering decomposition cases, when VMD is applied, the SVM-VMD-M03 combination achieved better results compared to other models (with scores r = 0.9859, NSE = 0.9717, KGE = 0.9755, PI = 0.0135, RSR = 0.1679, and RMSE = 0.1179). Conversely, with decomposition cases, when EMD applied, LSTM-EMD-M03 is explored as the more effective combination than others (with scores r = 0.9562, NSE = 0.9008, KGE = 0.9315, PI = 0.0256, RSR = 0.2978, and RMSE = 0.3143). The results demonstrate that GPR and SVM, coupled with VMD, yield high correlation (e.g., r ≈ 0.986) and low RMSE (~0.12), indicating the ability to reproduce FLake dynamics rather than as accurate predictions of measured bottom temperature. Full article
(This article belongs to the Special Issue Application of Machine Learning in Hydrological Monitoring)
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