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Signals
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Rainfall Estimation Using Signals
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Rainfall Estimation Using Signals

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3178

Special Issue Editors

Dr. Vasilis Christofilakis

E-Mail Website
Guest Editor
Electronics-Telecommunications and Applications Laboratory, Physics Department, University of Ioannina, 45110 Ioannina, Greece
Interests: software-defined radios and cooperative network systems; smart antennas—MIMO; digital signal processing; signal propagation; signal attenuation due to precipitation; schumann resonance measurements; object-oriented approaches for wireless systems
Special Issues, Collections and Topics in MDPI journals
Dr. Spyridon K. Chronopoulos

E-Mail Website
Guest Editor
Electronics-Telecommunications and Applications Laboratory, Physics Department, University of Ioannina, 45110 Ioannina, Greece
Interests: design and development of wireless and embedded systems; OFDM; turbo codes; antenna design; satellite links; measuring technology; mathematical analysis; modelling and interdisciplinary applications
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Peppas

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Peloponnese, 22100 Tripoli, Greece
Interests: wireless communications; digital communications; MIMO Systems; wireless; cooperative communications; cognitive radio
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hector E. Nistazakis

E-Mail Website
Guest Editor
Section of Electronic Physics and Systems, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece
Interests: wireless communication systems; free space optical communications (FSO); fiber optics communications; electronic physics; nonlinear optoelectronic circuits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rainfall is a weather phenomenon that has intensified in recent decades due to climate change. Extreme rainfall is directly related to natural hazards because of flash flood events. Therefore, accurate rainfall measurement in space and time with real-time notification of authorities and competent services regarding extreme events is imperative. 

However, accurate rainfall measurement is challenging, especially for extreme precipitation events characterized by high spatial variability. Typical instruments for measuring in situ rainfall are rain gauges and disdrometers, with further information regarding drops. Typical remote rainfall measurements are acquired by weather satellites and radars. Rainfall measurements based on weather radars suffer from several errors coming either from natural or technical sources. Concerning weather satellite measurements, a significant limitation is the indirect character of the retrieval that correlates microphysical and dynamical cloud characteristics with rain amounts at ground level.

In the last ten years, there has been a significant boost mainly in estimating the precipitation through backhaul cellular microwave links and optical links, with the main advantage of the existing infrastructure and significant restrictions regarding limited access to rainfall data. Also, the 5G future operation in the mmWave area will give even more accurate rainfall data. The rainfall measurement is of great interest through 5G and next-generation smartphones. The main advantage is the possibility of a high rainfall data real-time grid network. The more serious disadvantage is the limited measurement accuracy, restricted by the smartphone’s hardware.

Collecting and processing rainfall data is accomplished from various sources: rain gauge and DSD sensors, microwave backhaul networks, FSO communication links, weather radars, and satellites, and a dense network of data from smartphone devices is the holy grail of real-time accurate rainfall measurement. Much has been done, but certainly, much more is needed.

This Special Issue aims to gather researchers from different scientific areas to highlight new and future trends in this interdisciplinary field. This particular issue highlights works that propose new or improved traditional or opportunistic sensing methods, in situ and remote rainfall devices, methodologies, and algorithms. Challenges to be covered include limited power resolution, baseline fluctuations, wet antenna errors, atmospheric turbulence, heterogeneity in data, rain type classifications, calibration, and validation errors.

Furthermore, smart technologies should further enhance green growth, sustainability, and knowledgeable platforms. These should include the proposed methods to foster the viability of green technologies and their derivatives even more. Consequently, additional works from this field are welcomed and should incorporate mainly or partially the proposed ideas.

Dr. Vasilis Christofilakis
Dr. Spyridon K. Chronopoulos
Dr. Konstantinos Peppas
Prof. Dr. Hector E. Nistazakis
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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • signal strength
  • smartphones
  • rainfall algorithms
  • measurements
  • mmWave
  • microwave
  • attenuation
  • FSO links
  • weather radars
  • satellites
  • natural hazards
  • hazards and sustainability
  • green growth against natural disasters
  • green buildings
  • civil authorities
  • rain-gauges
  • disdrometers
  • IoT

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Published Papers (2 papers)

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Research

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31 pages, 1004 KiB  
Article
Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes
by Matteo Bodini
Signals 2024, 5(4), 659-689; https://doi.org/10.3390/signals5040037 - 10 Oct 2024
Viewed by 922
Abstract
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning [...] Read more.
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article
(This article belongs to the Special Issue Rainfall Estimation Using Signals)
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Review

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20 pages, 6885 KiB  
Review
A Review of Rainfall Estimation in Indonesia: Data Sources, Techniques, and Methods
by Maulana Putra, Mohammad Syamsu Rosid and Djati Handoko
Signals 2024, 5(3), 542-561; https://doi.org/10.3390/signals5030030 - 16 Aug 2024
Cited by 1 | Viewed by 1429
Abstract
Rainfall information with high spatial and temporal resolution are essential in various fields. Heavy rainfall in a short period can cause problems and disasters that result in loss of life and damage to property. Conversely, the absence of rain for an extended period [...] Read more.
Rainfall information with high spatial and temporal resolution are essential in various fields. Heavy rainfall in a short period can cause problems and disasters that result in loss of life and damage to property. Conversely, the absence of rain for an extended period can also have negative social and economic impacts. Data accuracy, wide spatial coverage, and high temporal resolution are challenges in obtaining rainfall information in Indonesia. This article presents information on data sources and methods for measuring rainfall and reviews the latest research regarding statistical algorithms and machine learning to estimate rainfall in Indonesia. Rainfall information in Indonesia was obtained from several sources. Firstly, the method of direct rainfall measurement conducted with both manual and automatic rain gauges was reviewed; however, this data source provided minimal results, with uneven spatial density. Secondly, the application of remote sensing estimation using both radar and weather satellites was reviewed. The estimated rainfall results obtained using remote sensing showed more comprehensive spatial coverage and higher temporal resolution. Finally, we reviewed rainfall products obtained from model calculations, using both statistical and machine learning by integrating measurement and remote sensing data. The results of the review demonstrated that rainfall estimation products applied in remote sensing using machine learning models have the potential to produce more accurate spatial and temporal data. However, the validation of rainfall data from direct measurements is required first. This research’s contribution can provide practitioners and researchers in Indonesia and the surrounding region with information on problems, challenges, and recommendations for optimizing rainfall measurement products using appropriate adaptive technology. Full article
(This article belongs to the Special Issue Rainfall Estimation Using Signals)
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