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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: 30 January 2026 | Viewed by 589

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

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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 (1 paper)

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Research

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
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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