Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Soft Computing Application for Sustainable Water Resource and Environmental Management
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Soft Computing Application for Sustainable Water Resource and Environmental Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 5091

Special Issue Editors

Dr. Chow Ming Fai

E-Mail Website
Guest Editor
Discipline of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Selangor, Malaysia
Interests: green infrastructures; sustainable urban stormwater management; flood prediction and mitigation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ahmed El-Shafie

E-Mail Website1 Website2
Guest Editor
Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Interests: water resources management; hydrological modeling; optimization algorithms; artificial intelligent and machne learning; dam operation
Special Issues, Collections and Topics in MDPI journals
Dr. Al Mahfoodh Ali Najah Ahmed

E-Mail Website
Guest Editor
Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
Interests: machine learning modelling; water quality modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Both the rapid urbanization process and  climate change have significantly posed greater engineering challenges in the 21st century. The real scenarios in this context are associated with uncertainty and vagueness. As the water and environmental systems become more complex, new approaches and techniques are required to enable the decision-makers and stakeholders to develop sustainable solutions and make decisions based on a wide range of uncertainty and limited information. Application of soft computing techniques to solve complex problems in hydrology and water resources fields have gained increasing attention due to their robustness and inherent tolerance of uncertainty compared to the traditional methods. Soft computing allows spatial and temporal integration of various data of different nature in order to acquire better empirical insights into complex multiparameter problems and, hence, encourage adaptive strategies for holistic water management. Some of the soft computing techniques, such as fuzzy logic, expert systems, artificial neural networks, fuzzy neural networks, and genetic algorithms, have been widely employed either as single or hybrid systems in solving various real-life water and environmental problems. Thus, the aim of this Special Issue is to show recent and novel applications of soft computing techniques in the field of water engineering, especially in various hydrological processes  (e.g., rainfall, runoff, etc.) and water quality forecasting. All original research and review contributions within the scope of this Special Issue are highly welcome.

Dr. Chow Ming Fai
Prof. Dr. Ahmed Hussein Kamel Ahmed Elshafie
Dr. Al Mahfoodh Ali Najah Ahmed
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. Sustainability 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 2400 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

  • water resource management
  • environmental modeling
  • genetic algorithms
  • machine learning
  • multi-purpose reservoir
  • hybrid expert systems
  • adaptive neuro fuzzy inference systems (ANFIS)
  • sustainable development
  • pattern recognition
  • optimization algorithm

Published Papers (2 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

17 pages, 1931 KiB  
Article
Spatio-Temporal Variations of Discharge and Sediment in Rivers Flowing into the Anzali Lagoon
by Sohrab Khalilivavdareh, Ali Shahnazari and Amirpouya Sarraf
Sustainability 2022, 14(1), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010507 - 04 Jan 2022
Cited by 3 | Viewed by 1881
Abstract
In the last few years, trend identification has become an important issue in hydrological time-series analyses; it is also a difficult task, due to the variety of models and the impact of climate change on the river flow regime. Due to the vital [...] Read more.
In the last few years, trend identification has become an important issue in hydrological time-series analyses; it is also a difficult task, due to the variety of models and the impact of climate change on the river flow regime. Due to the vital importance of the Anzali Lagoon to the environment of the region, and the threat to its health caused by the volume or amount of inlet sediments, we decided to study the changes in flow and sediment in the rivers flowing into the Anzali Lagoon. For the present study, the long-term monthly, seasonal, and annual sediment and discharge data of seven stations were obtained during the period 1985–2019. According to the available information, the trend of sediment load variation was investigated at different time scales. In this study, the Mann–Kendall statistical test, the double-mass curve, and performance fitting were used to assess the seasonal and annual trends in sediment and river flow. The results showed that at Aghamahale station, the low relationship between discharge and sediment compared with that at other stations was due to the low slope and constant water of the Behmbar River, which caused the sediments to settle and decreased their carrying rate. Moreover, Nokhaleh station had the largest share of sediment transfer to the lagoon during 2002–2012. Sediment details also show that the highest amount of sediment in all stations occurred in non-crop seasons—i.e., from October to January—and was directly dependent on the amount of rainfall in these areas. The results of the sediment analysis also indicate that the discharge and the subsequent sediment loads from upstream to downstream were high over the summer. Furthermore, the rivers downstream demonstrated springtime peaks in the sediment loads and discharge, probably owing to snow melting. Full article
(This article belongs to the Special Issue Soft Computing Application for Sustainable Water Resource and Environmental Management)
►▼ Show Figures

Figure 1

16 pages, 26144 KiB  
Article
A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation
by Majid Mirzaei, Haoxuan Yu, Adnan Dehghani, Hadi Galavi, Vahid Shokri, Sahar Mohsenzadeh Karimi and Mehdi Sookhak
Sustainability 2021, 13(23), 13384; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313384 - 03 Dec 2021
Cited by 19 | Viewed by 2197
Abstract
Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in [...] Read more.
Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor. Full article
(This article belongs to the Special Issue Soft Computing Application for Sustainable Water Resource and Environmental Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop