Hydrology

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

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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.

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Keywords

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

Published Papers (2 papers)

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19 pages, 4880 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/hydrology11030041 - 21 Mar 2024

Viewed by 1091

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 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

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/hydrology10080169 - 11 Aug 2023

Viewed by 1852

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 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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