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Water
Special Issues
Prediction and Assessment of Hydrological Processes
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Prediction and Assessment of Hydrological Processes

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2679

Special Issue Editors

Dr. Rana Muhammad Adnan

E-Mail Website
Guest Editor
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: Sustainable development; water resources management; hydrological modelling; artificial intelligence; time series analysis ;rainfall-runoff relationship; wind energy; sediment load; evaporation; evapotranspiration; hydro-meteorological droughts; groundwater; water quality parameters modeling; Novel meta-heuristic approaches applications; trend analysis; clustering; watershed planning and management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ozgur Kisi

grade E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting; estimating; spatial and temporal analysis of hydro-climatic variables such as precipitation; streamflow; suspended sediment; evaporation; evapotranspiration; groundwater; lake level and water quality parameters; hydro-informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Mo Wang

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Guangzhou University, Guangzhou, China
Interests: green infrastructure; low impact development; landscape planning; stormwater management; nature-based solution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will feature the latest advances and developments in sustainable hydrological cycling. Simulated hydrological responses of river basins remain highly uncertain, due to the presence of a broad variety of schematizations, erroneous measurements, and prior assumptions. Accurate and reliable runoff predictions made using the rainfall–runoff models should be a core component for flood risk management. However, since most areas around the world remain ungauged, identifying the parameters of rainfall–runoff models is still a challenge that may lead to the use of advance computational methods to overcome uncertainty in runoff predictions. In addition, in water resource management at present, there are different challenges and uncertainties caused by climate change and manmade interferences, so it can be very difficult to make decisions regarding this problem. Further, the mismanagement and sustainability of current and future water resource allocation methods are also concerns. Thus, it is important to use the newest technology and tools to improve and effectively develop sustainable management methods.

The main themes of this Special Issue include, but are not limited to, the following:

  • The use of advanced computing methods for precise hydrological variable forecasting (modeling streamflow, floods, sediment, air temperature, evaporation, evapotranspiration, etc.);
  • The utilization of advanced machine learning and deep learning models with ensemble models for solving hydrological problems;
  • The spatial and temporal modeling of hydrological variables with the aid of advanced computing models;
  • The coupling of data preprocessing techniques with machine learning and deep learning methods to capture noise and nonlinear hydrological variables;
  • The use and development of novel optimization algorithms with machine learning methods to enhance their computing abilities.

Dr. Rana Muhammad Adnan
Prof. Dr. Ozgur Kisi
Dr. Mo Wang
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
  • water resource management
  • machine learning
  • deep learning

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

Published Papers (3 papers)

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Research

24 pages, 3453 KiB  
Article
Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm
by Misbah Ikram, Hongbo Liu, Ahmed Mohammed Sami Al-Janabi, Ozgur Kisi, Wang Mo, Muhammad Ali and Rana Muhammad Adnan
Water 2024, 16(21), 3038; https://doi.org/10.3390/w16213038 - 23 Oct 2024
Viewed by 608
Abstract
For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for [...] Read more.
For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R2). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TNinf. The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Qw, pH, SS, TP, NH3-N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH3-N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TNinf) in full-scale wastewater treatment plants. Full article
(This article belongs to the Special Issue Prediction and Assessment of Hydrological Processes)
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22 pages, 3249 KiB  
Article
LSTM-Autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages
by Yungyeong Shin, Kwang Yoon Na, Si Eun Kim, Eun Ji Kyung, Hyun Gyu Choi and Jongpil Jeong
Water 2024, 16(18), 2631; https://doi.org/10.3390/w16182631 - 16 Sep 2024
Viewed by 1011
Abstract
The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak [...] Read more.
The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. Full article
(This article belongs to the Special Issue Prediction and Assessment of Hydrological Processes)
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24 pages, 5409 KiB  
Article
Spatiotemporal Dynamics of Ecosystem Water Yield Services and Responses to Future Land Use Scenarios in Henan Province, China
by Shuxue Wang, Tianyi Cai, Qian Wen, Chaohui Yin, Jing Han and Zhichao Zhang
Water 2024, 16(17), 2544; https://doi.org/10.3390/w16172544 - 9 Sep 2024
Viewed by 753
Abstract
Water yield (WY) service is the cornerstone of ecosystem functionality. Predicting and assessing the impact of land use/land cover (LULC) changes on WY is imperative for a nation’s food security, regional economic development, and ecological environmental protection. This study aimed to evaluate the [...] Read more.
Water yield (WY) service is the cornerstone of ecosystem functionality. Predicting and assessing the impact of land use/land cover (LULC) changes on WY is imperative for a nation’s food security, regional economic development, and ecological environmental protection. This study aimed to evaluate the water yield (WY) service in Henan Province, China, using high-resolution (30 m) remote sensing land use monitoring data from four study years: 1990, 2000, 2010, and 2020. It also utilized the PLUS model to predict the characteristics of LULC evolution and the future trends of WY service under four different development scenarios (for 2030 and 2050). The study’s results indicated the following: (1) From 1990 to 2020, the Henan Province’s WY first increased and then decreased, ranging from 398.56 × 108 m3 to 482.95 × 108 m3. The southern and southeastern parts of Henan Province were high-value WY areas, while most of its other regions were deemed low-value WY areas. (2) The different land use types were ranked in terms of their WY capacity, from strongest to weakest, as follows: unused land, cultivated land, grassland, construction land, woodland, and water. (3) The four abovementioned scenarios were ranked, from highest to lowest, based on the Henan’s total WY (in 2050) in each of them: high-quality development scenario (HDS), business-as-usual scenario (BAU), cultivated land protection scenario (CPS), and ecological protection scenario (ES). This study contributes to the advancement of ecosystem services research. Its results can provide scientific support for water resource management, sustainable regional development, and comprehensive land-use planning in Henan Province. Full article
(This article belongs to the Special Issue Prediction and Assessment of Hydrological Processes)
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