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Hydrology
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Big Data and Machine Learning in Hydrology: Recent Advances and...
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Big Data and Machine Learning in Hydrology: Recent Advances and Trends

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 7779

Special Issue Editor

Dr. Huidae Cho

E-Mail Website
Guest Editor
Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: smart infrastructure; water resources engineering; hydrologic modeling; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are living in an era of big data thanks to advances in data collection technologies such as high-resolution remote sensing and sensor networks of the Internet of Things (IoT). Traditionally, researchers have been using physically based models and statistical analysis. However, uncertainty arises inevitably from a lack of data in many parts of the world and our incomplete understanding of physical processes. To address this issue in hydrologic prediction, the International Association of Hydrological Sciences (IAHS) initiated Predictions in Ungauged Basins (PUB) in the early 2000s. While their initiative focused on improved understandings of the hydrological cycle for better physically based modeling, this Special Issue aims to deal with an abundance of data from any sources. Data abundance can be a blessing if we can make sense of it, or it can be an inconvenience because of the challenges in data management. This Special Issue calls for research on recent advances and trends in hydrology using machine learning techniques to extract useful information from big data.

Topics of interest include, but are not limited to:

  • New big data management techniques;
  • Uncertainty quantification in big data;
  • Machine learning for information extraction from big data;
  • Big data and machine learning applications for hydrologic forecasting;
  • Big data assimilation and aggregation into already trained machine learning models;
  • Hybrid approaches using big data, machine learning, and physically based models.

Dr. Huidae Cho
Guest Editor

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. Hydrology is an international peer-reviewed open access monthly 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 1800 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

  • big data computing
  • machine learning
  • deep learning
  • time series analysis
  • hydrologic forecasting
  • hydrologic feature extraction

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Published Papers (4 papers)

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Research

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19 pages, 2692 KiB  
Article
RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis
by Arash Adib, Mohammad Pourghasemzadeh and Morteza Lotfirad
Hydrology 2024, 11(9), 155; https://doi.org/10.3390/hydrology11090155 - 19 Sep 2024
Cited by 1 | Viewed by 759
Abstract
In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of [...] Read more.
In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range. Full article
(This article belongs to the Special Issue Big Data and Machine Learning in Hydrology: Recent Advances and Trends)
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19 pages, 4880 KiB  
Article
A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events
by Benjamin Burrichter, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
Hydrology 2024, 11(3), 41; https://doi.org/10.3390/hydrology11030041 - 21 Mar 2024
Cited by 1 | Viewed by 2339
Abstract
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 975 manholes. As part [...] Read more.
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 975 manholes. As part of the investigations, the TFT was compared to other deep learning architectures to evaluate its predictive performance. In addition to precipitation measurements and forecasts, the issue of how the additional consideration of measurements in the sewer network as model inputs impacts forecast accuracy was investigated. A varying number of sensors and different measurement signals were compared. The results indicate high performance for the TFT compared to other model architectures like a long short-term memory (LSTM) network or a dual-stage attention-based recurrent neural network (DA-RNN). Additionally, results suggest that considering a single measuring point at the outlet of the sewer network instead of an entire measuring network yields better forecasts. One possible explanation is the high correlation between measurements, which increases model and training complexity without adding much value. Full article
(This article belongs to the Special Issue Big Data and Machine Learning in Hydrology: Recent Advances and Trends)
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20 pages, 2498 KiB  
Article
Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data
by Hamza Salahudin, Muhammad Shoaib, Raffaele Albano, Muhammad Azhar Inam Baig, Muhammad Hammad, Ali Raza, Alamgir Akhtar and Muhammad Usman Ali
Hydrology 2023, 10(8), 169; https://doi.org/10.3390/hydrology10080169 - 11 Aug 2023
Cited by 3 | Viewed by 2623
Abstract
To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological [...] Read more.
To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R2 Multan = 0.97, R2 Skardu = 0.99, R2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance. Full article
(This article belongs to the Special Issue Big Data and Machine Learning in Hydrology: Recent Advances and Trends)
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Review

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26 pages, 4773 KiB  
Review
Applications of Machine Learning and Remote Sensing in Soil and Water Conservation
by Ye Inn Kim, Woo Hyeon Park, Yongchul Shin, Jin-Woo Park, Bernie Engel, Young-Jo Yun and Won Seok Jang
Hydrology 2024, 11(11), 183; https://doi.org/10.3390/hydrology11110183 - 30 Oct 2024
Viewed by 1046
Abstract
The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At [...] Read more.
The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation. Full article
(This article belongs to the Special Issue Big Data and Machine Learning in Hydrology: Recent Advances and Trends)
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