Suchergebnisse - Hydrological Modeling using Machine Learning Methods

Hydrodynamic Investigation of Laguna Lake, Philippines for Water Security and Flood Risk Management of Metro Manila von Eugene C. Herrera, Kensuke Naito

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Long short-term memory models of water quality in inland water environments von Juncheol Pyo, Yakov Pachepsky, Soobin Kim, Ather Abbas, Minjeong Kim, Young-Mi Kwon, Mayzonee Ligaray, Kyung Hwa Cho

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Comparative study on the calculation methods of ecological base flow in a mountainous river von Cheng Gao, Manqiu Hao, Lianghong Song, Rong Wang, Shuaibing Shao, Ying Huang, Yufa Guo, Xueyao Liu

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A Sustainable Early Warning System Using Rolling Forecasts Based on ANN and Golden Ratio Optimization Methods to Accurately Predict Real-Time Water Levels and Flash Flood von Feras Alasali, Rula Tawalbeh, Zahra Ghanem, Fatima Mohammad, Mohammad Alghazzawi

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The importance of artificial intelligence-based methods in precipitation modeling studies: a bibliometric analysis von Aydin, Olgu, Kilar, Hatice

“… In this context, using Artificial Intelligence (AI), Artificial Neural Networks (ANN), Machine Learning (ML), and Deep Learning …”
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Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods von Yang, Yang, Chui, Ting Fong May

“… This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data …”
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Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany von Schmidt, Lennart, Heße, Falk, Attinger, Sabine, Kumar, Rohini

“… Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever …”
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Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region von Huynh, Ngo Nghi Truyen, Garambois, Pierre‐André, Colleoni, François, Renard, Benjamin, Roux, Hélène, Demargne, Julie, Jay‐Allemand, Maxime, Javelle, Pierre

“… Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data …”
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Towards Interpretable Physical‐Conceptual Catchment‐Scale Hydrological Modeling Using the Mass‐Conserving‐Perceptron von Wang, Yuan‐Heng, Gupta, Hoshin V.

“… We investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment …”
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AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods von Abbas, Ather, Boithias, Laurie, Pachepsky, Yakov, Kim, Kyunghyun, Chun, Jong Ahn, Cho, Kyung Hwa

“… advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process …”
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Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach von Wang, Lei, Li, Yi, Biswas, Asim, Zhao, Yong, Niu, Ben, Siddique, Kadambot.H.M.

“… Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management …”
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Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms von Band, Shahab S., Janizadeh, Saeid, Chandra Pal, Subodh, Saha, Asish, Chakrabortty, Rabin, Melesse, Assefa M., Mosavi, Amirhosein

“… Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested …”
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Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow von Reis, Guilherme Barbosa, da Silva, Demetrius David, Fernandes Filho, Elpídio Inácio, Moreira, Michel Castro, Veloso, Gustavo Vieira, Fraga, Micael de Souza, Pinheiro, Sávio Augusto Rocha

“… There are different methods for predicting streamflow, and, recently machine learning has been widely used for this purpose …”
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Assessing future hydrological and sediment transport response of an urban watershed using a machine learning–based land cover change model von Peker, İsmail Bilal, Cuceloglu, Gokhan, Sökmen, Eren Dağra, Gülbaz, Sezar

“… Modeling LCC using machine learning techniques enhances the ability to generate realistic future scenarios, providing a robust basis for informed watershed management decisions …”
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Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques von Karimizadeh, Keivan, Yi, Jaeeung

“… The main methods were using the Coupled Model Intercomparison Project phase 6 (CMIP6), the Soil and Water Assessment Tool (SWAT …”
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Improving the spatial resolution of GRACE-based groundwater storage estimates using a machine learning algorithm and hydrological model von Yin, Wenjie, Zhang, Gangqiang, Liu, Futian, Zhang, Dasheng, Zhang, Xiuping, Chen, Sheming

“… The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional …”
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Regionalization of hydrological model parameters using gradient boosting machine von Song, Zhihong, Xia, Jun, Wang, Gangsheng, She, Dunxian, Hu, Chen, Hong, Si

“… The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins …”
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Evaluation of Machine Learning Techniques for Hydrological Drought Modeling: A Case Study of the Wadi Ouahrane Basin in Algeria von Achite, Mohammed, Jehanzaib, Muhammad, Elshaboury, Nehal, Kim, Tae-Woong

“… In this study, various machine learning (ML) techniques including four methods (i.e., ANN, ANFIS, SVM, and DT …”
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Exploring the Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models von Ding, Bingbing, Yu, Xinxiao, Jia, Guodong

“… This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Random Forest (RF …”
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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling von Houénafa, Sianou Ezéckiel, Johnson, Olatunji, Ronoh, Erick K., Moore, Stephen E.

“… Accurately simulating river discharge remains a challenge. Hybrid models combining hydrological models with machine learning improve discharge simulation and offer better interpretability than standalone machine learning models …”
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