Submit to Water Review for Water Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Intelligent Modelling for Hydrology and Water Resources

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 13820

Share This Special Issue

Special Issue Editors

Prof. Dr. Wenchuan Wang

E-Mail Website
Guest Editor

College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: watershed hydrological models; hydrological forecasting under changing environment; data preprocessing techniques; hybrid intelligent computing; climate change; modelling hydrological processes
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhongkai Feng

E-Mail Website
Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: reservoir operation; hydrological forecasting; water resources management; artificial intelligence; engineering optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Mingwei Ma

E-Mail Website
Guest Editor

North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: climate change; extreme hydrological event; land surface process; multivariate statistics; uncertainty and risk analysis; hydrological modelling under changing environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, hydrology and water resources have been facing increasing scientific and technical problems under the impacts of climate changes and human activities. To guarantee the suitable operation and planning of water resources, it is important to develop more effective methods for hydrology and water resources problems. With the rapid development of information technologies, machine learning methods and artificial intelligence technologies are providing new possibilities for solving various engineering problems. Against this background, many scientists and engineers are working to develop novel methods that can help create reasonable scheduling schemes and policies for hydrology and water resources problems in the changing environment.

This Special Issue aims to provide an opportunity for scholars to share their latest research findings related to hydrology and water resources and other related topics. In this Special Issue, high-quality research papers concerning the following themes are invited, but not limited to:

Watershed hydrological model;
Hydrological process modeling;
Flood warning and risk analysis;
Hydrological forecasting and simulation;
Extreme hydrological and climate events;
Impact of climate changes on hydrological process;
Smart water resources management and planning;
Optimal reservoir(s) operation;
Extreme hydro-meteorological events;
Dynamical mechanisms associated with hydro-meteorological processes;
New approaches/methods/models for hydrology and water resources;
Relevant case studies and applications.

Prof. Dr. Wenchuan Wang
Prof. Dr. Zhongkai Feng
Dr. Mingwei Ma
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
hydrological forecasting
artificial intelligence
water resources
hydrological process
uncertainty and risk
optimal reservoir operation
data-driven techniques
optimization algorithm

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

26 pages, 11410 KiB

Open AccessArticle

Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

by Yiyang Wang, Wenchuan Wang, Hongfei Zang and Dongmei Xu

Water 2023, 15(22), 3928; https://doi.org/10.3390/w15223928 - 10 Nov 2023

Cited by 1 | Viewed by 990

Abstract

The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash–Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Figure 1

18 pages, 5035 KiB

Open AccessArticle

Study on the Snowmelt Flood Model by Machine Learning Method in Xinjiang

by Mingqiang Zhou, Wenjing Lu, Qiang Ma, Han Wang, Bingshun He, Dong Liang and Rui Dong

Water 2023, 15(20), 3620; https://doi.org/10.3390/w15203620 - 16 Oct 2023

Cited by 1 | Viewed by 900

Abstract

There are many mountain torrent disasters caused by melting icebergs and snow in Xinjiang, which are very different from traditional mountain torrent disasters. Most of the areas affected by snowmelt are in areas without data, making it very difficult to predict and warn of disasters. Taking the Lianggoushan watershed at the southern foot of Boroconu Mountain as the research subject, the key factors were screened by Pearson correlation coefficient and the factor analysis method, and the data of rainfall, water level, temperature, air pressure, wind speed, and snow depth were used as inputs, respectively, with support vector regression (SVR), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory neural network (LSTM) models used to simulate the daily average water level at the outlet of the watershed. The research results showed that the root mean square error (RMSE) values of SVR, RF, KNN, ANN, RNN, and LSTM in the training period were 0.033, 0.012, 0.016, 0.022, 0.011, and 0.010, respectively, and in the testing period they were 0.075, 0.072, 0.071, 0.075, 0.075, and 0.071, respectively. The performance of LSTM was better than that of other models, but it had more hyperparameters that needed to be optimized. The performance of RF was second only to LSTM; it had only one hyperparameter and was very easy to determine. The RF model showed that the simulation results mainly depended on the average wind speed and average sea level pressure data. The snowmelt model based on machine learning proposed in this study can be widely used in iceberg snowmelt warning and forecasting in ungauged areas, which is of great significance for the improvement of mountain flood prevention work in Xinjiang. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Figure 1

20 pages, 3080 KiB

Open AccessArticle

Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach

by Pavan Kumar Yeditha, G. Sree Anusha, Siva Sai Syam Nandikanti and Maheswaran Rathinasamy

Water 2023, 15(18), 3244; https://doi.org/10.3390/w15183244 - 12 Sep 2023

Viewed by 874

Abstract

In the present work, a wavelet-based multiscale deep learning approach is developed to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. The proposed multiscale method was tested and validated over the Krishna River basin in India. The results from the proposed methods were compared with contemporary models based on Multiple Linear Regression and Neural Networks. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The wavelet-based methods yielded results with 13–34% reduced error when compared with the best single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast rainfall with reasonable accuracy for different climate zones of the country. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Figure 1

14 pages, 2754 KiB

Open AccessArticle

The Application and Applicability of HEC-HMS Model in Flood Simulation under the Condition of River Basin Urbanization

by Xiaolong Yu and Jing Zhang

Water 2023, 15(12), 2249; https://doi.org/10.3390/w15122249 - 15 Jun 2023

Cited by 6 | Viewed by 2737

Abstract

With the acceleration of urbanization in a river basin, the changes in the underlying surface structure of the basin are more and more intense, which causes frequent floods. This article aims to analyze the applicability of the HEC-HMS model in flood simulation in urbanization basins and the influence of land use changes on catchment runoff. Pu River Basin is a typical urbanization basin and is taken as the research project. Based on land use changes, soil types, and long-term hydrological data in the Pu River Basin, the HEC-HMS hydrological model is constructed using GIS and HEC-geoHMS. Then, the relative error of flood peak and runoff, Nash–Sutcliffe efficiency coefficient, and correlation are used to evaluate the model simulation rating. The results show that the HEC-HMS model is suitable for an urbanization basin, and its performance grade before urbanization is better than that after urbanization. Finally, sensitivity analysis of nine parameters on model performance shows that curve number, initial abstraction, imperviousness, and time lag are the main parameters. The research results will provide a reference for urbanization basins’ flood simulation and stormwater management. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Figure 1

24 pages, 5035 KiB

Open AccessArticle

Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

by Mohammad Reza Goodarzi, Amir Reza R. Niknam, Ali Barzkar, Majid Niazkar, Yahia Zare Mehrjerdi, Mohammad Javad Abedi and Mahnaz Heydari Pour

Water 2023, 15(10), 1876; https://doi.org/10.3390/w15101876 - 15 May 2023

Cited by 15 | Viewed by 4369

Abstract

Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller’s diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Figure 1

16 pages, 5194 KiB

Open AccessArticle

Estimation of Real-Time Rainfall Fields Reflecting the Mountain Effect of Rainfall Explained by the WRF Rainfall Fields

by Jeonghoon Lee, Okjeong Lee, Jeonghyeon Choi, Jiyu Seo, Jeongeun Won, Suhyung Jang and Sangdan Kim

Water 2023, 15(9), 1794; https://doi.org/10.3390/w15091794 - 7 May 2023

Cited by 3 | Viewed by 1526

Abstract

The effect of mountainous regions with high elevation on hourly timescale rainfall presents great difficulties in flood forecasting and warning in mountainous areas. In this study, the hourly rainfall–elevation relationship of the regional scale is investigated using the hourly rainfall fields of three storm events simulated by Weather Research and Forecasting (WRF) model. From this relationship, a parameterized model that can estimate the spatial rainfall field in real time using the hourly rainfall observation data of the ground observation network is proposed. The parameters of the proposed model are estimated using eight representative pixel pairs in valleys and mountains. The proposed model was applied to the Namgang Dam watershed, a representative mountainous region in the Korea, and it was found that as elevation increased in eight selected pixel pairs, rainfall intensity also increased. The increase in rainfall due to the mountain effect was clearly observed with more rainfall in high mountainous areas, and the rainfall distribution was more realistically represented using an algorithm that tracked elevation along the terrain. The proposed model was validated using leave-one-out cross-validation with seven rainfall observation sites in mountainous areas, and it demonstrated clear advantages in estimating a spatial rainfall field that reflects the mountain effect. These results are expected to be helpful for flood forecasting and warning, which need to be calculated quickly, in mountainous areas. Considering the importance of orographic effects on rainfall spatial distribution in mountainous areas, more storm events and physical analysis of environmental factors (wind direction, thermal cycles, and mountain slope angle) should be continuously studied. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Figure 1

24 pages, 4156 KiB

Open AccessFeature PaperArticle

Multi-Reservoir Flood Control Operation Using Improved Bald Eagle Search Algorithm with ε Constraint Method

by Wenchuan Wang, Weican Tian, Kwokwing Chau, Hongfei Zang, Mingwei Ma, Zhongkai Feng and Dongmei Xu

Water 2023, 15(4), 692; https://doi.org/10.3390/w15040692 - 9 Feb 2023

Cited by 9 | Viewed by 1761

Abstract

The reservoir flood control operation problem has the characteristics of multiconstraint, high-dimension, nonlinearity, and being difficult to solve. In order to better solve this problem, this paper proposes an improved bald eagle search algorithm (CABES) coupled with ε-constraint method (ε-CABES). In order to test the performance of the CABES algorithm, a typical test function is used to simulate and verify CABES. The results are compared with the bald eagle algorithm and particle swarm optimization algorithm to verify its superiority. In order to further test the rationality and effectiveness of the CABES method, two single reservoirs and a multi-reservoir system are selected for flood control operation, and the ε constraint method and the penalty function method (CF-CABES) are compared, respectively. Results show that peak clipping rates of ε-CABES and CF-CABES are both 60.28% for Shafan Reservoir and 52.03% for Dahuofang Reservoir, respectively. When solving the multi-reservoir joint flood control operation system, only ε-CABES flood control operation is successful, and the peak clipping rate is 51.76%. Therefore, in the single-reservoir flood control operation, the penalty function method and the ε constraint method have similar effects. However, in multi-reservoir operation, the ε constraint method is better than the penalty function method. In summary, the ε-CABES algorithm is more reliable and effective, which provides a new method for solving the joint flood control scheduling problem of large reservoirs. Full article

(This article belongs to the Special Issue Intelligent Modelling for Hydrology and Water Resources)

► Show Figures

Journal Menu

Journal Browser

Intelligent Modelling for Hydrology and Water Resources

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI