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Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process

Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper,...

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Main Authors: Chong K.L., Huang Y.F., Koo C.H., Sherif M., Ahmed A.N., El-Shafie A.
Other Authors: 57208482172
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2024
Subjects:
Deep learning algorithms
Hyperparameter optimization
Machine learning algorithms
Streamflow forecasting
Forecasting
Long short-term memory
Regression analysis
Stream flow
Time series analysis
Water resources
Automated searches
Cross entropy
Deep learning algorithm
Entropy-based
Hyper-parameter
Hyper-parameter optimizations
Machine learning problem
Streamflow forecast
algorithm
empirical analysis
entropy
machine learning
optimization
peak flow
streamflow
water management
Learning algorithms
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spelling my.uniten.dspace-346702024-10-14T11:21:36Z Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process Chong K.L. Huang Y.F. Koo C.H. Sherif M. Ahmed A.N. El-Shafie A. 57208482172 55807263900 57204843657 7005414714 57214837520 16068189400 Deep learning algorithms Hyperparameter optimization Machine learning algorithms Streamflow forecasting Forecasting Long short-term memory Regression analysis Stream flow Time series analysis Water resources Automated searches Cross entropy Deep learning algorithm Entropy-based Hyper-parameter Hyper-parameter optimizations Machine learning algorithms Machine learning problem Streamflow forecast Streamflow forecasting algorithm empirical analysis entropy machine learning optimization peak flow streamflow water management Learning algorithms Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. Due to the distinctive characteristics of these two adopted forms, selecting the correct algorithm for the machine learning problem along with their hyperparameter tuning process is critical to the realization of the desired results. For the distinct streamflow formulated scenarios, three neural network algorithms and their hyperparameter tuning strategy were investigated. The comparative empirical studies had revealed that formulated categorical-based streamflow forecast is a better choice than a regression-based streamflow forecast, regardless of the algorithms used for instance, the f1-score of 0.7 (categorical based) is obtained compared to the 0.53 (regression based) for the LSTM in scenario 1 (binary). Furthermore, forest-based algorithms were investigated and shown to be superior at forecasting high streamflow fluctuations in situations featuring low-dimensional streamflow input. Besides, encoding the streamflow time series as images (input) for forecasting purposes would require a thorough analysis as there is a discrepancy in the results, revealing that not all approaches are suitable for streamflow image transformation. The functional ANOVA analysis provided evidence to substantiate the Bayesian optimization results, implying that the hyperparameters were effectively optimized. � 2022, The Author(s). Final 2024-10-14T03:21:36Z 2024-10-14T03:21:36Z 2023 Article 10.1007/s13201-022-01790-5 2-s2.0-85141876820 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141876820&doi=10.1007%2fs13201-022-01790-5&partnerID=40&md5=9db022d5eb620b7cf3a1e17f2ae136f7 https://irepository.uniten.edu.my/handle/123456789/34670 13 1 6 All Open Access Gold Open Access Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Deep learning algorithms
Hyperparameter optimization
Machine learning algorithms
Streamflow forecasting
Forecasting
Long short-term memory
Regression analysis
Stream flow
Time series analysis
Water resources
Automated searches
Cross entropy
Deep learning algorithm
Entropy-based
Hyper-parameter
Hyper-parameter optimizations
Machine learning algorithms
Machine learning problem
Streamflow forecast
Streamflow forecasting
algorithm
empirical analysis
entropy
machine learning
optimization
peak flow
streamflow
water management
Learning algorithms
spellingShingle Deep learning algorithms
Hyperparameter optimization
Machine learning algorithms
Streamflow forecasting
Forecasting
Long short-term memory
Regression analysis
Stream flow
Time series analysis
Water resources
Automated searches
Cross entropy
Deep learning algorithm
Entropy-based
Hyper-parameter
Hyper-parameter optimizations
Machine learning algorithms
Machine learning problem
Streamflow forecast
Streamflow forecasting
algorithm
empirical analysis
entropy
machine learning
optimization
peak flow
streamflow
water management
Learning algorithms
Chong K.L.
Huang Y.F.
Koo C.H.
Sherif M.
Ahmed A.N.
El-Shafie A.
Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
description Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. Due to the distinctive characteristics of these two adopted forms, selecting the correct algorithm for the machine learning problem along with their hyperparameter tuning process is critical to the realization of the desired results. For the distinct streamflow formulated scenarios, three neural network algorithms and their hyperparameter tuning strategy were investigated. The comparative empirical studies had revealed that formulated categorical-based streamflow forecast is a better choice than a regression-based streamflow forecast, regardless of the algorithms used
author2 57208482172
author_facet 57208482172
Chong K.L.
Huang Y.F.
Koo C.H.
Sherif M.
Ahmed A.N.
El-Shafie A.
format Article
author Chong K.L.
Huang Y.F.
Koo C.H.
Sherif M.
Ahmed A.N.
El-Shafie A.
author_sort Chong K.L.
title Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
title_short Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
title_full Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
title_fullStr Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
title_full_unstemmed Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
title_sort investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061189797249024
score 13.209306

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