Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.0
Journals
Water
Special Issues
Application of Big Data and Deep Learning in Hydrological Modelling,...
share announcement

Application of Big Data and Deep Learning in Hydrological Modelling, Flood and Drought Monitoring

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

Deadline for manuscript submissions: 20 January 2025 | Viewed by 7330

Special Issue Editors

Dr. Heng Lu

E-Mail Website1 Website2
Guest Editor
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
Interests: water and soil resources and the environment; natural disaster prevention; remote sensing; geographic information system; geographic model; deep learning; transfer learning; water body extraction
Dr. Li Zhou

E-Mail Website
Guest Editor
Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China
Interests: hydrological modelling; flood forecasting; flood risk management; machine learning; remote sensing; flash flood
Prof. Dr. Mohamed Rasmy

E-Mail Website
Guest Editor
International Centre for Water Hazard and Risk Management, Public Works Research Institute, Tsukuba, Japan
Interests: hydrological modeling and prediction; satellite remote sensing; water resources management; weather and climate predictions; satellite data assimilation methods

Special Issue Information

Dear Colleagues,

The synergy of abundant digital data and rapid advances in deep learning has uniquely positioned us to enhance hydrological models and flood/drought monitoring. This Special Issue converges hydrology, data science and AI to explore how big data (e.g., remote sensing, reanalysis data, in situ monitoring, etc.) and deep learning can bolster hydrological modelling, flood prediction and drought tracking.

Our aim is to curate a comprehensive collection of articles showcasing inventive methodologies, case studies and applications. These innovations integrate big data and deep learning in hydrological processes, introducing novel models, algorithms and frameworks that harness vast datasets and advanced machine learning to refine the accuracy, efficiency and reliability of hydrological predictions.

This Special Issue bridges the gap between conventional hydrological modelling and emerging data-driven approaches. By offering a platform for researchers to exchange insights, it contributes to ongoing discussions on sustainable water management, disaster resilience and climate adaptation. Ultimately, this compilation advances our understanding of how the synergy of big data and deep learning can reshape hydrology, benefiting both scientific progress and practical flood/drought management. We look forward to receiving your contributions.

Dr. Heng Lu
Dr. Li Zhou
Prof. Dr. Mohamed Rasmy
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

  • hydrological modeling
  • big data analytics
  • deep learning techniques
  • flood prediction
  • drought monitoring
  • data-driven hydrology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 13995 KiB  
Article
An Index Used to Evaluate the Applicability of Mid-to-Long-Term Runoff Prediction in a Basin Based on Mutual Information
by Shuai Xie, Zhilong Xiang, Yongqiang Wang, Biqiong Wu, Keyan Shen and Jin Wang
Water 2024, 16(11), 1619; https://doi.org/10.3390/w16111619 - 5 Jun 2024
Viewed by 726
Abstract
Accurate and reliable mid-to-long-term runoff prediction (MLTRP) is of great importance in water resource management. However, the MLTRP is not suitable in each basin, and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index [...] Read more.
Accurate and reliable mid-to-long-term runoff prediction (MLTRP) is of great importance in water resource management. However, the MLTRP is not suitable in each basin, and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index is developed in this study based on the predictor selection method using mutual information (MI) and partial MI (PMI). The relationship between the TMI and the predictive performance of five AI models is analyzed by applying five models to 222 forecasting scenarios in Australia. This results in over 222 forecasting scenarios which demonstrate that, compared with the MI, the developed TMI index can better represent the available information in the predictors and has a more significant negative correlation with the RRMSE, with a correlation coefficient between −0.62 and −0.85. This means that the model’s predictive performance will become better along with the increase in TMI, and therefore, the developed TMI index can be used to evaluate the applicability of MLTRP. When the TMI is more than 0.1, the available information in the predictors can support the construction of MLTRP models. In addition, the TMI can be used to partly explain the differences in predictive performance among five models. In general, the complex models, which can better utilize the contained information, are more sensitive to the TMI and have more significant improvement in terms of predictive performance along with the increase in TMI. Full article
(This article belongs to the Special Issue Application of Big Data and Deep Learning in Hydrological Modelling, Flood and Drought Monitoring)
►▼ Show Figures

Figure 1

24 pages, 4368 KiB  
Article
Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis
by Matthew G. Montgomery, Miles B. Yaw and John S. Schwartz
Water 2024, 16(6), 865; https://doi.org/10.3390/w16060865 - 17 Mar 2024
Viewed by 1161
Abstract
Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. [...] Read more.
Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 102 to 5.68 × 103) than estimates that assume coincident failures are the result of independent hydrologic events. Full article
(This article belongs to the Special Issue Application of Big Data and Deep Learning in Hydrological Modelling, Flood and Drought Monitoring)
►▼ Show Figures

Figure 1

18 pages, 3389 KiB  
Article
Spatiotemporal Changes and Hazard Assessment of Hydrological Drought in China Using Big Data
by Yi Tao, Erhao Meng and Qiang Huang
Water 2024, 16(1), 106; https://doi.org/10.3390/w16010106 - 27 Dec 2023
Cited by 4 | Viewed by 1437
Abstract
The intensification of the regional water cycle resulting from climate change, coupled with the influence of human activities, has brought about alterations in the frequency, scale, and intensity of droughts. In this study, based on hydrological big data and the standardized runoff drought [...] Read more.
The intensification of the regional water cycle resulting from climate change, coupled with the influence of human activities, has brought about alterations in the frequency, scale, and intensity of droughts. In this study, based on hydrological big data and the standardized runoff drought index (SRI), the multi-scale spatiotemporal evolution of hydrological drought in China from 1948 to 2014 was analyzed using the run-length theory and gravity center model. Meanwhile, the hydrological drought hazard index was constructed to analyze the distribution of the hazard levels of drought in China. The results showed that, during 1948~2014, there was an opposite spatial distribution between the average intensity and the average coverage–duration–frequency of drought in the Yellow River Basin, Haihe River Basin and southeastern river basins. The drought situation in most river basins in China has shown an aggravating trend, among which the southeastern river basins, Haihe River Basin, Songliao River Basin and Pearl River Basin have generally shown an aggravating trend. The drought situation in China was severe in the 1950s and 1960s, gradually reduced in the 1970s, 1980s, and 1990s, and the drought situation was the mildest in the 1990s. After entering the 21st century, the drought situation began to worsen sharply. Meanwhile, from 1948 to 2014, the hazard level of drought generally presented a pattern of high in the west and north, and low in the east and south. The hazard levels of drought in the northwest and northeast were generally higher, and those in the southwest and southeast regions were generally lower. In general, the hazard levels of drought were relatively high in most areas of China. Full article
(This article belongs to the Special Issue Application of Big Data and Deep Learning in Hydrological Modelling, Flood and Drought Monitoring)
►▼ Show Figures

Figure 1

23 pages, 7720 KiB  
Article
Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin
by Silang Nimai, Yufeng Ren, Tianqi Ao, Li Zhou, Hanxu Liang and Yanmin Cui
Water 2023, 15(21), 3758; https://doi.org/10.3390/w15213758 - 27 Oct 2023
Cited by 2 | Viewed by 1503
Abstract
Runoff simulation is an ongoing challenge in the field of hydrology. Process-based (PB) hydrological models often gain unsatisfactory simulation accuracy due to incomplete physical process representations. While the deep learning (DL) models demonstrate their capacity to grasp intricate hydrological response processes, they still [...] Read more.
Runoff simulation is an ongoing challenge in the field of hydrology. Process-based (PB) hydrological models often gain unsatisfactory simulation accuracy due to incomplete physical process representations. While the deep learning (DL) models demonstrate their capacity to grasp intricate hydrological response processes, they still face constraints pertaining to the representative training data and comprehensive hydrological observations. In order to provide unobservable hydrological variables from the PB model to the DL model, this study constructed hybrid models by feeding the output variables of the PB model (BTOP) into the DL model (LSTM) as additional input features. These variables underwent feature dimensionality reduction using the feature selection method (Pearson Correlation Coefficient, PCC) and the feature extraction method (Principal Component Analysis, PCA) before input into LSTM. The results showed that the standalone LSTM performed well across the basin, with NSE values all exceeding 0.70. The hybrid models enhanced the simulation performance of the standalone LSTM. The NSE values increased from 0.75 to nearly 0.80 in a sub-basin. Lastly, if the BTOP output is directly fed into LSTM without feature dimensionality reduction, the model’s accuracy significantly decreases due to noise interference. The NSE value decreased by 0.09 compared to the standalone LSTM in a sub-basin. The results demonstrated the effectiveness of PCC and PCA in removing redundant information within hydrological variables. These findings provide new insights into incorporating physical information into LSTM and constructing hybrid models. Full article
(This article belongs to the Special Issue Application of Big Data and Deep Learning in Hydrological Modelling, Flood and Drought Monitoring)
►▼ Show Figures

Figure 1

20 pages, 8050 KiB  
Article
Improving Hydrological Simulation Accuracy through a Three-Step Bias Correction Method for Satellite Precipitation Products with Limited Gauge Data
by Xing Liu, Zhengwei Yong, Lingxue Liu, Ting Chen, Li Zhou and Jidong Li
Water 2023, 15(20), 3615; https://doi.org/10.3390/w15203615 - 16 Oct 2023
Cited by 5 | Viewed by 1783
Abstract
Satellite precipitation products (SPPs) have advanced remarkably in recent decades. However, the bias correction of SPPs still performs unsatisfactorily in the case of a limited rain-gauge network. This study proposes a new real-time bias correction approach that includes three steps to improve the [...] Read more.
Satellite precipitation products (SPPs) have advanced remarkably in recent decades. However, the bias correction of SPPs still performs unsatisfactorily in the case of a limited rain-gauge network. This study proposes a new real-time bias correction approach that includes three steps to improve the precipitation quality with limited gauges and facilitate the hydrological simulation in the Min River Basin, China. This paper employed 66 gauges as available ground observation precipitation, Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) as the historical precipitation to correct Global Satellite Mapping of Precipitation NOW (GNOW) and Global Satellite Mapping of Precipitation NRT (GNRT) in 2020. A total of 1020 auto-rainfall stations were used as the benchmark to evaluate the original and corrected SPPs with six criteria. The results show that the statistic and dynamic bias correction method (SDBC) improved the SPPs significantly and the cumulative distribution function matching method (CDF) could reduce the overcorrection error from SDBC. The inverse error variance weighting method (IEVW) integrations of GNOW and GNRT did not have noticeable improvement as they use similar hardware and software processes. The corrected SPPs show better performance in hydrological simulations. It is recommended to employ different SPPs for integration. The proposed bias correction approach is significant for precipitation estimation and flood prediction in data-sparse basins worldwide. Full article
(This article belongs to the Special Issue Application of Big Data and Deep Learning in Hydrological Modelling, Flood and Drought Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop