Soft Computing in Hydrology: Application of Machine Learning, Optimization Algorithms, and Data Mining

A special issue of Hydrology (ISSN 2306-5338).

Deadline for manuscript submissions: closed (2 January 2022) | Viewed by 8048

Special Issue Editor


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Guest Editor
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden
Interests: surface water hydrology; snow hydrology; remote sensing; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

In recent years, appropriate approaches such as soft computing have been widely used in many research areas and applications and it proved a suitable performance in environmental sciences such as hydrology and water resources management. Soft computing allows us to understand more about water resources management; However, soft computing approaches can give to researchers a new view for solving the challenges and overcoming the water resources management issues. In addition, the high capability of soft computing such as big data handling, handling the complexity of problems, high simulation speed, and high accuracy results can be used for future potential researches in hydrological studies. In this Special Issue editor would like to invite research works which incorporate soft computing techniques in hydrology and water resources management, such as (but not restricted to):

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Water resources management by soft computing approaches

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Hydrological modeling: application of soft computing

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Using optimization algorithms for water managing

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Data mining in hydrological studies

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Studying about irrigation efficiency by soft computing approaches

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Water management challenges: solutions by soft computing approaches

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Integrated time series analysis techniques for hydrological studies

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Improving precision of hydrologic models by soft computing approaches

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Decision making in water resources projects

Dr. Babak Mohammadi
Guest Editor

Manuscript Submission Information

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Keywords

  • Applied Artificial Intelligence
  • Data Mining
  • Hydrological Modeling
  • Machine Learning
  • Optimization Algorithm
  • Time Series Analysis
  • Water Resource Management

Published Papers (2 papers)

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Research

15 pages, 3265 KiB  
Article
Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms
by Saeid Mehdizadeh, Babak Mohammadi and Farshad Ahmadi
Hydrology 2022, 9(1), 9; https://doi.org/10.3390/hydrology9010009 - 1 Jan 2022
Cited by 13 | Viewed by 2484
Abstract
Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series [...] Read more.
Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series of the studied stations were modeled through the other effective variables comprising minimum air temperature (Tmin), extraterrestrial radiation (Ra), vapor pressure deficit (VPD), sunshine duration (n), and relative humidity (RH). The correlation coefficients between the input and output parameters were utilized to determine the most effective inputs. Furthermore, novel hybrid models were proposed in this study in order to increase the estimation accuracy of Tdew. For this purpose, two optimization algorithms named bee colony optimization (BCO) and dragonfly algorithm (DFA) were coupled on the classic ANFIS. It was concluded that the hybrid models (i.e., ANFIS-BCO and ANFIS-DFA) demonstrated better performances compared to the classic ANFIS. The full-input pattern of the coupled models, specifically the ANFIS-DFA, was found to present the most accurate results for both the selected stations. Therefore, the developed hybrid models can be proposed as alternatives to the classic ANFIS to accurately estimate the daily Tdew. Full article
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12 pages, 2307 KiB  
Communication
Governance of Artificial Intelligence in Water and Wastewater Management: The Case Study of Japan
by Tomoko Takeda, Junko Kato, Takashi Matsumura, Takeshi Murakami and Amila Abeynayaka
Hydrology 2021, 8(3), 120; https://doi.org/10.3390/hydrology8030120 - 11 Aug 2021
Cited by 6 | Viewed by 4365
Abstract
The integration of artificial intelligence into various aspects of daily life is developing at a rapid pace in Japan. Discussions to govern applications of artificial intelligence to the field of social infrastructure are also critical and need to match the rapid pace of [...] Read more.
The integration of artificial intelligence into various aspects of daily life is developing at a rapid pace in Japan. Discussions to govern applications of artificial intelligence to the field of social infrastructure are also critical and need to match the rapid pace of development. However, the legal implications and risks of applying artificial intelligence to the management of lifelines such as drinking water supply and wastewater treatment have not yet been fully explored. This paper reviews the existing legislations and ongoing discussions on governance regarding applications of artificial intelligence to water and wastewater management. Based on the review, we discuss the ability of legislative frameworks in Japan to respond to the applications of artificial intelligence, as well as identifying potential gaps and challenges thereof, including access to accurate data, demarcation of rights and responsibilities, risk hedging and risk management, monitoring and evaluation, and handling of intellectual property rights. This paper concludes with key recommendations to national and local governments to support the application of artificial intelligence in the field of water and wastewater. Full article
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