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Water
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Drought Monitoring and Modeling Utilizing Advanced Machine Learning Models
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Drought Monitoring and Modeling Utilizing Advanced Machine Learning Models

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 8301

Special Issue Editors

Dr. Saeid Mehdizadeh

E-Mail Website
Guest Editor
Water Engineering Department, Urmia University, Urmia, Iran
Interests: hydrological modeling; drought; Evapotranspiration; river streamflow; machine learning models; wavelet analysis; artificial intelligence; hybrid models
Dr. Farshad Ahmadi

E-Mail Website
Guest Editor
Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Interests: drought monitoring; modeling; water resources management; machine learning; trend analysis

Special Issue Information

Dear Colleagues,

Drought is usually considered a natural hazard that can be caused by a decrease in rainfall and an increase in ambient air temperature. It can cause significant changes in the water resources, agriculture, and hydrology of an area. Many drought indices have been developed and proposed for monitoring the drought status of a particular location, which can be categorized into agricultural, meteorological, and hydrological droughts. In recent years, machine learning models have attracted significant attention among scholars when monitoring and modeling the droughts.

This Special Issue aims to report recent advances in the forecasting of various drought indices, including standardized precipitation index (SPI), standardized precipitation evapotranspiration Index (SPEI), reconnaissance drought index (RDI), and Palmer’s drought severity index (PDSI), etc., applying machine learning models. In this context, hybrid paradigms of machine learning models are highly recommended.

Dr. Saeid Mehdizadeh
Dr. Farshad Ahmadi
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.

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Keywords

  • drought indices
  • forecasting
  • machine learning models
  • hybrid techniques
  • standardized precipitation index
  • standardized precipitation evapotranspiration index
  • reconnaissance drought index
  • Palmer’s drought severity index

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Published Papers (3 papers)

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Research

13 pages, 4583 KiB  
Article
A New Multi-Objective Genetic Programming Model for Meteorological Drought Forecasting
by Masoud Reihanifar, Ali Danandeh Mehr, Rifat Tur, Abdelkader T. Ahmed, Laith Abualigah and Dominika Dąbrowska
Water 2023, 15(20), 3602; https://doi.org/10.3390/w15203602 - 14 Oct 2023
Cited by 8 | Viewed by 1896
Abstract
Drought forecasting is a vital task for sustainable development and water resource management. Emerging machine learning techniques could be used to develop precise drought forecasting models. However, they need to be explicit and simple enough to secure their implementation in practice. This article [...] Read more.
Drought forecasting is a vital task for sustainable development and water resource management. Emerging machine learning techniques could be used to develop precise drought forecasting models. However, they need to be explicit and simple enough to secure their implementation in practice. This article introduces a novel explicit model, called multi-objective multi-gene genetic programming (MOMGGP), for meteorological drought forecasting that addresses both the accuracy and simplicity of the model applied. The proposed model considers two objective functions: (i) root mean square error and (ii) expressional complexity during its evolution. While the former is used to increase the model accuracy at the training phase, the latter is assigned to decrease the model complexity and achieve parsimony conditions. The model evolution and verification procedure were demonstrated using the standardized precipitation index obtained for Burdur City, Turkey. The comparison with benchmark genetic programming (GP) and multi-gene genetic programming (MGGP) models showed that MOMGGP provides the same forecasting accuracy with more parsimony conditions. Thus, it is suggested to utilize the model for practical meteorological drought forecasting. Full article
(This article belongs to the Special Issue Drought Monitoring and Modeling Utilizing Advanced Machine Learning Models)
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13 pages, 4265 KiB  
Article
Modeling Rainwater Harvesting and Storage Dynamics of Rural Impoundments in Dry Chaco Rangelands
by Marcos Javier Niborski, Osvaldo Antonio Martin, Francisco Murray, Esteban Gabriel Jobbágy, Marcelo Daniel Nosetto, Ricardo Andrés Paez and Patricio Nicolás Magliano
Water 2023, 15(13), 2353; https://doi.org/10.3390/w15132353 - 25 Jun 2023
Cited by 1 | Viewed by 1817
Abstract
Transporting water to supply livestock is one of the great challenges of the drylands. Ranchers usually make impoundments, filled by runoff, to access freshwater for cattle supply in flat rangelands. The aim of this study was to understand rainfall-runoff generation and water storage [...] Read more.
Transporting water to supply livestock is one of the great challenges of the drylands. Ranchers usually make impoundments, filled by runoff, to access freshwater for cattle supply in flat rangelands. The aim of this study was to understand rainfall-runoff generation and water storage temporal dynamics of impoundments in the Dry Chaco rangelands (Argentina). Thus, we instrumented six impoundments over three consecutive years and analyzed water storage data by developing a probabilistic model. For all impoundments, the rainfall event size thresholds to generate runoff presented values between 15 and 33 mm. Once they reached this threshold, the water gain response slopes presented values between 19 and 99 m3 mm−1. Loss patterns of water storage were described by exponential or linear functions. The predicted water storage dynamics presented high accuracy with the observed time series for all impoundments (RMSD between 380 and 1320 m3). The model only needs daily rainfall and air temperature to be run, making it easy to be used by scientists, ranchers, or local decision makers. It may be used to explore the hydrological functioning of small and seasonal water bodies of different sites of the world exposed to drought episodes caused by high climate variability and/or climate change. Full article
(This article belongs to the Special Issue Drought Monitoring and Modeling Utilizing Advanced Machine Learning Models)
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Graphical abstract

22 pages, 3674 KiB  
Article
Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data
by Reham R. Mostafa, Ozgur Kisi, Rana Muhammad Adnan, Tayeb Sadeghifar and Alban Kuriqi
Water 2023, 15(3), 486; https://doi.org/10.3390/w15030486 - 25 Jan 2023
Cited by 49 | Viewed by 3901
Abstract
Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency [...] Read more.
Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data, minimum temperature, maximum temperature, and extraterrestrial radiation. The outcomes of the hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, and RVM-QANA models compared with single RVFL and RVM models. Various input combinations and three data split scenarios were employed. The results revealed that the AHA and QANA considerably improved the efficiency of RVFL and RVM methods in modeling ET0. Considering the periodicity component and extraterrestrial radiation as inputs improved the prediction accuracy of the applied methods. Full article
(This article belongs to the Special Issue Drought Monitoring and Modeling Utilizing Advanced Machine Learning Models)
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