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

Deadline for manuscript submissions: closed (10 January 2025) | Viewed by 17686

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Dr. Guangyuan Kan

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Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, China
Interests: hydrological–hydrodynamic modelling; groundwater modelling; parallel computing; artificial intelligence; remote sensing
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Special Issue Information

Dear Colleagues,

With the development of modern artificial intelligence and parallel computing, the applications of these novel technologies in the field of hydrological and hydrodynamic modeling, flood simulation and forecasting, risk and uncertainty analysis, etc., have significantly improved accuracy, reliability, and computational efficiency in the domain of disaster defense. This Special Issue mainly focuses on the application and novel methods of flood simulation, forecasting and modeling, model parameter estimation, risk and uncertainty analysis, and data analysis, based on modern artificial intelligence and/or parallel computing technologies. We invite submissions including, but not limited to, the following topics:

(1) Artificial-intelligence-aided hydrological simulation and forecasting.

(2) Hydrodynamic modelling based on artificial intelligence technologies.

(3) Flood risk analysis, hydrological or hydrodynamic model uncertainty analysis based on intelligence optimization algorithms or other related artificial intelligence techniques.

(4) Model parameter optimization algorithms based on intelligence optimization algorithms.

(5) Data analysis based on artificial intelligence or machine leaning technologies.

(6) Hydrological/hydrodynamic modeling, risk and uncertainty analysis, parameter optimization, data analysis, etc., accelerated by modern parallel computing technologies such as many-core GPU, multi-core CPU, or large-scale parallel computer clusters.

Dr. Guangyuan Kan
Guest Editor

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Keywords

artificial intelligence
machine learning
parallel computing
GPU computing
hydrological model
hydrodynamic model
flood simulation
flood forecasting
risk and uncertainty analysis
data analysis

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Published Papers (11 papers)

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Research

21 pages, 1772 KiB

Open AccessArticle

Determination of Temporary Hubs Locations Along a River in Case of Flood

by Suhad Rebhi Al-Natoor, Gergely Kovács, Zoltán Lakner and Béla Vizvári

Water 2025, 17(9), 1268; https://doi.org/10.3390/w17091268 - 24 Apr 2025

Viewed by 158

Abstract

Due to global climate change, the frequency and intensity of floods will be increasing in the decades to come. Under these conditions, there is an urgent need to develop such relatively simple and robust models and methods, which help the logistical preparatory and crisis management work in case of this natural disaster. In the crisis management phase, the integrated complex command centers and logistical hubs play an essential role. It is an open-ended question: how do we determine the optimal location of these hubs, and find an optimal compromise between their radius of supply and vulnerability? The current article presents a simple and fast method to determine the optimal position of hubs, minimizing their vulnerability, in cases when there is no chance to control the flood of the river (no dam), and in cases when there is a natural or artificial barrier, preventing the flow of water (dam scenario). Based on a system of equations, applying the Gumbel distribution of maximal water levels in various years, the article offers numerical examples to prove the simplicity and practical applicability of the method developed. This approach can supply a decision support system, based on AI. The paper concludes with policy implications. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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17 pages, 28541 KiB

Open AccessArticle

Utilizing Deep Learning Models to Predict Streamflow

by Habtamu Alemu Workneh and Manoj K. Jha

Water 2025, 17(5), 756; https://doi.org/10.3390/w17050756 - 5 Mar 2025

Viewed by 715

Abstract

This study employs convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU) deep learning models to simulate daily streamflow using precipitation data. Two approaches were explored: one without dimension reduction and another incorporating dimensionality reduction technique. Principal component analysis (PCA) was employed for dimensionality reduction, and partial autocorrelation function (PACF) was used to determine time lags. An augmented Dickey–Fuller (ADF) test was utilized to ascertain the stationarity of the data, ensuring optimal model performance. The data were normalized and then partitioned into features and target variables, before being split into training, validation, and test sets. The developed models were tested for their performance, robustness, and stability at three locations along the Neuse River, which is in the Neuse River Basin, North Carolina, USA, covering an area of about 14,500 km². Furthermore, the model’s performance was tested during peak flood events to assess their ability to capture the temporal resolution of streamflow. The results revealed that the CNN model could capture the variability in daily streamflow prediction, as evidenced by excellent statistical measures, including mean absolute error, root mean square error, and Nush–Sutcliffe efficiency. The study also found that incorporating dimensionality reduction significantly improved model performance. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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21 pages, 17554 KiB

Open AccessArticle

Hydrological Modeling to Unravel the Spatiotemporal Heterogeneity and Attribution of Baseflow in the Yangtze River Source Area, China

by Huazhun Ren, Guangdong Wu, Longcang Shu, Wenjian Tang, Chengpeng Lu, Bo Liu, Shuyao Niu, Yunliang Li and Yuxuan Wang

Water 2024, 16(20), 2892; https://doi.org/10.3390/w16202892 - 11 Oct 2024

Viewed by 1113

Abstract

Revealing the spatiotemporal variation in baseflow and its underlying mechanisms is critical for preserving the health and ecological functions of alpine rivers, but this has rarely been conducted in the source region of the Yangtze River (SRYR). Our study employed the Soil and Water Assessment Tool (SWAT) model coupled with two-parameter digital filtering and geostatistical approaches to obtain a visual representation of the spatiotemporal heterogeneity characteristics of the baseflow and baseflow index (BFI) in the SRYR. The SWAT model and multiple linear regression model (MLR) were used to quantitatively estimate the contribution of climate change and human activities to baseflow and BFI changes. The results underscore the robust applicability of the SWAT model within the SRYR. Temporally, the precipitation, temperature, and baseflow exhibited significant upward trends, and the baseflow and BFI showed contrasting intra-annual distribution patterns, which were unimodal and bimodal distribution, respectively. Spatially, the baseflow increased from northwest to southeast, and from the watershed perspective, the Tongtian River exhibited higher baseflow values compared to other regions of the SRYR. The baseflow and BFI values of the Dangqu River were greater than those of other tributaries. More than 50% of the entire basin had an annual BFI value greater than 0.7, which indicates that baseflow was the major contributor to runoff generation. Moreover, the contributions of climate change and human activities to baseflow variability were 122% and −22%, and to BFI variability, 60% and 40%. Specifically, precipitation contributed 116% and 60% to the baseflow and BFI variations, while the temperature exhibited contributions of 6% and 8%, respectively. Overall, it was concluded that the spatiotemporal distributions of baseflow and the BFI are controlled by various factors, and climate change is the main factor of baseflow variation. Our study offers valuable insights for the management and quantitative assessment of groundwater resources within the SRYR amidst climate change. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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14 pages, 2387 KiB

Open AccessArticle

A Forecast Heuristic of Back Propagation Neural Network and Particle Swarm Optimization for Annual Runoff Based on Sunspot Number

by Feifei Sun, Xinchuan Lu, Mingwei Yang, Chao Sun, Jinping Xie and Dong Sheng

Water 2024, 16(19), 2737; https://doi.org/10.3390/w16192737 - 26 Sep 2024

Viewed by 705

Abstract

Runoff prediction is of great importance to water utilization and water-project regulation. Although sun activity has been considered an important factor in runoff, little modeling has been constructed. This study put forward a forecast heuristic combining back propagation neural network (BPNN) and particle swarm optimization (PSO) for annual runoff based on sunspot number and applied it to the Yellow River of China for the period 1956–2016 and assessed the contribution of the sunspot number by placing sole BPNN modeling on the time series as a contrast. First, the heuristic is made up of BPNN calibration and PSO optimization: (1) we use historical data to calibrate BPNN models and obtain a prediction of the sunspot number for training and testing stages; (2) we use the PSO to minimize the difference between the predicted runoff of both BPNN and a linear equation for forecasting stage. Second, the application offers interesting findings: (1) while BPNN calibration obtains first-class forecasting with the ratio >85% with <20% absolute error in training and testing stages, the PSO can achieve similar performance in the forecasting stage; (2) the heuristic can achieve better prediction in years with a lower sunspot number; (3) besides the influence of the sun activity, atmospheric circulation, water usage, and water-project regulation do play important roles on the measured or natural runoff to some extent. This study could provide useful insights into further forecasting of measured and natural runoff under this forecast heuristic in the world. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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18 pages, 1788 KiB

Open AccessArticle

Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques

by Syeda Zehan Farzana, Dev Raj Paudyal, Sreeni Chadalavada and Md Jahangir Alam

Water 2024, 16(15), 2107; https://doi.org/10.3390/w16152107 - 25 Jul 2024

Cited by 2 | Viewed by 1419

Abstract

Changes in water quality are closely linked to seasonal fluctuations in streamflow, and a thorough understanding of how these variations interact across different time scales is important for the efficient management of surface water bodies such as rivers, lakes, and reservoirs. The aim of this study is to explore the potential connection between streamflow, rainfall, and water quality and propose an optimised ensemble model for the prediction of a water quality index (WQI). This study modelled the changes in five water quality parameters such as ammonia nitrogen (NH₃-N), phosphate (PO₄³⁻), pH, turbidity, total dissolved solids (TDS), and their associated WQI caused by rainfall and streamflow. The analysis was conducted across three temporal scales, weekly, monthly, and seasonal, using a generalised additive model (GAM) in Toowoomba, Australia. TDS, turbidity, and WQI exhibited a significant nonlinear variation with the changes in streamflow in the weekly and monthly scales. Additionally, pH demonstrated a significant linear to weakly linear correlation with discharge across the three temporal scales. For the accurate prediction of WQI, this study proposed an ensemble model integrating an extreme gradient boosting (XGBoost) and Bayesian optimisation (BO) algorithm, using streamflow as an input across the same temporal scales. The results for the three temporal scales provided the best accuracy of monthly data, based on the accuracy metrics R² (0.91), MAE (0.20), and RMSE (0.42). The comparison between the test and predicted data indicated that the prediction model overestimated the WQI at some points. This study highlights the efficiency of integrating rainfall, streamflow, and water quality correlations for WQI prediction, which can provide valuable insights for guiding future water management strategies in similar catchment areas, especially amidst changing climatic conditions. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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23 pages, 26729 KiB

Open AccessArticle

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

by Qingzhen Sun, Dehua Zhu, Zhaoyang Zhang and Jingbo Xu

Water 2024, 16(13), 1771; https://doi.org/10.3390/w16131771 - 21 Jun 2024

Cited by 2 | Viewed by 2011

Abstract

In recent years, urban waterlogging disasters have become increasingly prominent. Physically based urban waterlogging simulation models require considerable computational time. Therefore, rapid and accurate simulation and prediction of urban pluvial floods are important for disaster prevention and mitigation. For this purpose, we explored an urban waterlogging prediction method based on a long short-term memory neural network model that integrates an attention mechanism and a 1D convolutional neural network (1DCNN–LSTM–Attention), using the diversion of the Jinshui River in Zhengzhou, China, as a case study. In this method, the 1DCNN is responsible for extracting features from monitoring data, the LSTM is capable of learning from time-series data more effectively, and the Attention mechanism highlights the impact of features on input effectiveness. The results indicated the following: (1) The urban waterlogging rapid prediction model exhibited good accuracy. The Pearson correlation coefficient exceeded 0.95. It was 50–100 times faster than the InfoWorks ICM model. (2) Diversion pipelines can meet the design flood standard of a 200-year return period, aligning with the expected engineering objectives. (3) River channel diversion significantly reduced the extent of inundation. Under the 30-year return period rainfall scenario, the maximum inundation area decreased by 1.46 km², approximately equivalent to 205 international standard soccer fields. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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14 pages, 2803 KiB

Open AccessArticle

Forecasting the River Water Discharge by Artificial Intelligence Methods

by Alina Bărbulescu and Liu Zhen

Water 2024, 16(9), 1248; https://doi.org/10.3390/w16091248 - 26 Apr 2024

Cited by 8 | Viewed by 1882

Abstract

The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)—backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)—for the monthly data series discharge of the Buzău River, in Romania. The models were built for three periods: January 1955–September 2006 (S1 series), January 1955–December 1983 (S2 series), and January 1984–December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAE_Training = 5.02 and MAE_Test = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R² value, the best model was LSTM on S2 (R²_Training = 99.92%, and R²_Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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28 pages, 2857 KiB

Open AccessArticle

Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms

by Dilip Kumar Roy, Tasnia Hossain Munmun, Chitra Rani Paul, Mohamed Panjarul Haque, Nadhir Al-Ansari and Mohamed A. Mattar

Water 2023, 15(20), 3624; https://doi.org/10.3390/w15203624 - 16 Oct 2023

Cited by 9 | Viewed by 2357

Abstract

Accurate groundwater level (GWL) forecasts are crucial for the efficient utilization, strategic long-term planning, and sustainable management of finite groundwater resources. These resources have a substantial impact on decisions related to irrigation planning, crop selection, and water supply. This study evaluates data-driven models using different machine learning algorithms to forecast GWL fluctuations for one, two, and three weeks ahead in Bangladesh’s Godagari upazila. To address the accuracy limitations inherent in individual forecasting models, a Bayesian model averaging (BMA)-based heterogeneous ensemble of forecasting models was proposed. The dataset encompasses 1807 weekly GWL readings (February 1984 to September 2018) from four wells, divided into training (70%), validation (15%), and testing (15%) subsets. Both standalone models and ensembles employed a Minimum Redundancy Maximum Relevance (MRMR) algorithm to select the most influential lag times among candidate GWL lags up to 15 weeks. Statistical metrics and visual aids were used to evaluate the standalone and ensemble GWL forecasts. The results consistently favor the heterogeneous BMA ensemble, excelling over standalone models for multi-step ahead forecasts across time horizons. For instance, at GT8134017, the BMA approach yielded values like R (0.93), NRMSE (0.09), MAE (0.50 m), IOA (0.96), NS (0.87), and a-20 index (0.94) for one-week-ahead forecasts. Despite a slight decline in performance with an increasing forecast horizon, evaluation indices confirmed the superior BMA ensemble performance. This ensemble also outperformed standalone models for other observation wells. Thus, the BMA-based heterogeneous ensemble emerges as a promising strategy to bolster multi-step ahead GWL forecasts within this area and beyond. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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16 pages, 5505 KiB

Open AccessArticle

Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm

by Yuanyuan Liu, Yesen Liu, Shu Liu, Hancheng Ren, Peinan Tian and Nana Yang

Water 2023, 15(20), 3596; https://doi.org/10.3390/w15203596 - 14 Oct 2023

Viewed by 1565

Abstract

Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the time series is short. In contrast, historical rainfall data have a longer sample time series but lower precision. This study introduced a PCA algorithm to reconstruct historical rainfall data. Based on the temporal and spatial characteristics of rainfall extracted from high-resolution rainfall data over the past decade, historical (6 h intervals) rainfall spatial data were reconstructed into high-resolution (1 h intervals) spatial data to satisfy the requirements of the urban flood risk analysis. The results showed that the average error between the reconstructed data and measured values in the high-value area was within 15% and in the low-value area was within 20%, representing decreases of approximately 65% and 40%, respectively, compared to traditional interpolation data. The reconstructed historical spatial rainfall data conformed to the temporal and spatial distribution characteristics of rainfall, improved the granularity of rainfall spatial data, and enabled the effective and reasonable extraction and summary of the fine temporal and spatial distribution characteristics of rainfall. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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29 pages, 6523 KiB

Open AccessArticle

Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

by Xichen Liu, Guangyuan Kan, Liuqian Ding, Xiaoyan He, Ronghua Liu and Ke Liang

Water 2023, 15(15), 2700; https://doi.org/10.3390/w15152700 - 26 Jul 2023

Cited by 1 | Viewed by 1857

Abstract

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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25 pages, 8240 KiB

Open AccessArticle

Research on Rain Pattern Classification Based on Machine Learning: A Case Study in Pi River Basin

by Xiaodi Fu, Guangyuan Kan, Ronghua Liu, Ke Liang, Xiaoyan He and Liuqian Ding

Water 2023, 15(8), 1570; https://doi.org/10.3390/w15081570 - 17 Apr 2023

Cited by 8 | Viewed by 2623

Abstract

For the purpose of improving the scientific nature, reliability, and accuracy of flood forecasting, it is an effective and practical way to construct a flood forecasting scheme and carry out real-time forecasting with consideration of different rain patterns. The technique for rain pattern classification is of great significance in the above-mentioned technical roadmap. With the rapid development of artificial intelligence technologies such as machine learning, it is possible and necessary to apply these new methods to assist rain classification applications. In this research, multiple machine learning methods were adopted to study the time-history distribution characteristics and conduct rain pattern classification from observed rainfall time series data. Firstly, the hourly rainfall data between 2003 and 2021 of 37 rain gauge stations in the Pi River Basin were collected to classify rain patterns based on the universally acknowledged dynamic time warping (DTW) algorithm, and the classifications were treated as the benchmark result. After that, four other machine learning methods, including the Decision Tree (DT), Long- and Short-Term Memory (LSTM) neural network, Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM), were specifically selected to establish classification models and the model performances were compared. By adjusting the sampling size, the influence of different sizes on the classification was analyzed. Intercomparison results indicated that LightGBM achieved the highest accuracy and the fastest training speed, the accuracy and F₁ score were 98.95% and 98.58%, respectively, and the loss function and accuracy converged quickly after only 20 iterations. LSTM and SVM have satisfactory accuracy but relatively low training efficiency, and DT has fast classification speed but relatively low accuracy. With the increase in the sampling size, classification results became stable and more accurate. Besides the higher accuracy, the training efficiency of the four methods was also improved. Full article

(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)

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