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Article
Engineering, Civil
Hanlin Li et al.
Summary: This study proposes an improved Bootstrap method and combines it with three commonly used parameter estimation methods, i.e., improved Bootstrap with method of moments (IBMOM), maximum likelihood estimation (IBMLE), and maximum entropy principle (IBMEP). A series of numerical experiments and a case study on the estimation of distribution parameters demonstrate that the proposed methods provide more accurate and less deviated results compared to conventional Bootstrap and without-Bootstrap approaches. Moreover, the improved Bootstrap method shows significant improvement in parameter estimation when smaller sample size is used. The method based on improved Bootstrap offers a new solution to the requirement of large sample size in quality hydrological frequency analysis.
WATER RESOURCES MANAGEMENT
(2023)
Article
Engineering, Civil
Shengli Liao et al.
Summary: Accurate long-term runoff forecasting is crucial, but the current challenges include difficulty in model construction, poor prediction accuracy, and time-consuming forecasting processes. This study proposes a hybrid framework that combines antecedent inflow, specific meteorological factors, ensemble empirical mode decomposition (EEMD), artificial neural network (ANN), and a parallel algorithm. The framework transforms the input series into stationary signals, filters out closely related meteorological factors, and improves forecasting efficiency. The results show that the addition of meteorological factors greatly enhances the prediction models, and the EEMD-ANN model outperforms the ordinary ANN model under the same conditions. The optimization time of the 32-core model can be reduced by as much as 25 times, significantly saving forecast time.
WATER RESOURCES MANAGEMENT
(2023)
Article
Engineering, Civil
Djerbouai Salim et al.
Summary: Recently, the coupled Wavelet transform and Neural Networks models (WANN) have been widely used in hydrological drought forecasting, which is crucial for drought risk management. The study focuses on the effects of different discrete wavelet families and the level of decomposition on the performance of WANN models in drought forecasting for long lead times. The results show that WANN models with discrete approximation of Meyer perform the best, with maximum forecast lead times of 36 months for SPI-12, 18 months for SPI-6, and 7 months for SPI-3. Drought forecasting for long lead times is highly significant for drought risk and water resources management.
WATER RESOURCES MANAGEMENT
(2023)
Article
Computer Science, Software Engineering
Kouao Laurent Kouadio et al.
Summary: Water exploration primarily relies on hydro-geophysics, combining geophysical methods with hydrogeological techniques to determine drilling locations and the permeability coefficient (k) parameter. However, unsuccessful boreholes and difficulties in data collection result in significant losses for funders, geophysical and drilling ventures. To address these issues, watex library offers efficient algorithms and smart approaches, including recovering lost EM signals, automatic drilling location detection, flow rate prediction, and machine learning-based mixture learning strategy, to minimize losses in future hydro-geophysical engineering projects.
Article
Engineering, Environmental
Beyene Akirso Alehu et al.
Summary: The impact of climate change on the water balance of Lake Hawassa catchment in southern Ethiopia was assessed, showing an expected increase in precipitation and surface runoff, and a decrease in lateral discharge and water yield. Constant monitoring and updated sustainable water resource management and development is necessary.
ENVIRONMENTAL PROCESSES-AN INTERNATIONAL JOURNAL
(2023)
Review
Engineering, Civil
Mohamed Hamitouche et al.
Summary: This paper provides a comprehensive review of AI methods for predicting high-flow extremes. It identifies ANNs, SVMs, wavelets, and Bayesian methods as the most suitable methods at all-time scales, and suggests exploring the potential of other methods.
WATER RESOURCES MANAGEMENT
(2022)
Article
Environmental Sciences
Kouao Laurent Kouadio et al.
Summary: This study applied support vector machines (SVMs) to predict flow rates in groundwater exploration, aiming to minimize unsuccessful drillings. The SVM models achieved prediction accuracies of 77% and 83% on multiclass and binary datasets, respectively. The use of optimal polynomial and radial basis function kernels resulted in higher accuracies of 81.61% and 87.36%. Learning curves showed that larger training data could improve prediction performance on the multiclass dataset, but not necessarily on the binary dataset.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Jina Yin et al.
Summary: This study introduces a novel machine learning-based groundwater ensemble modeling framework combined with Bayesian model averaging to predict groundwater storage change in agricultural regions with improved reliability.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Engineering, Civil
Haidong Huang et al.
Summary: Research has found that using ensemble learning methods for short-term water demand forecasting is rarely explored in the field of water distribution systems. The proposed method involves two models for water demand forecasting, which significantly improves the accuracy and stability of predictions.
WATER RESOURCES MANAGEMENT
(2021)
Review
Engineering, Civil
Mohammad Zounemat-Kermani et al.
Summary: There is a growing trend in employing ensemble learning methodologies in various engineering fields, including hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available in hydrological sciences has led to the development and utilization of different strategies. The general findings suggest the superiority of using ensemble strategies over traditional model learning in hydrology, with boosting techniques being more commonly and successfully implemented compared to other methods.
JOURNAL OF HYDROLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Zeng Li et al.
METEOROLOGY AND ATMOSPHERIC PHYSICS
(2020)
Article
Environmental Sciences
Tidar Bayu et al.
WATER RESOURCES RESEARCH
(2020)
Article
Green & Sustainable Science & Technology
Phong Tung Nguyen et al.
Article
Engineering, Civil
Peyman Yariyan et al.
WATER RESOURCES MANAGEMENT
(2020)
Article
Multidisciplinary Sciences
Wasiu Olanrewaju Raji et al.
Article
Engineering, Environmental
Zahra Jamshidzadeh
ENVIRONMENTAL PROCESSES-AN INTERNATIONAL JOURNAL
(2020)
Article
Environmental Sciences
Donghwan Kim et al.
REMOTE SENSING OF ENVIRONMENT
(2019)
Article
Environmental Sciences
S. Sahoo et al.
WATER RESOURCES RESEARCH
(2017)
Article
Engineering, Civil
Mustafa Erkan Turan et al.
WATER RESOURCES MANAGEMENT
(2016)
Article
Environmental Sciences
E. K. White et al.
WATER RESOURCES RESEARCH
(2016)
Article
Biochemical Research Methods
S Varma et al.
BMC BIOINFORMATICS
(2006)
Article
Environmental Sciences
H Bjornlund
WATER RESOURCES RESEARCH
(2004)
