On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran

Merufinia, Edris; Sharafati, Ahmad; Abghari, Hirad; Hassanzadeh, Yousef

doi:10.1007/s12517-022-11045-x

Cited by 10 publications

(1 citation statement)

References 80 publications

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“…The scientific time series data for discharge, temperature, and precipitation were gathered on a daily basis. After organizing the data, the University of Waikato models were implemented using the Weka 3.8.6 application [49]. The software was utilized for two rounds of training and testing to determine the optimal combination for each model.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

et al. 2023

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Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

et al. 2023

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Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction

Iqbal,

Siddiqi

2023

Stoch Environ Res Risk Assess

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Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data

Kisi,

Heddam,

Parmar

et al. 2024

Stoch Environ Res Risk Assess

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This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it with single MARS, M5 model tree (M5Tree), random forest regression (RF), multilayer perceptron neural network (MLP). In the first modeling stage, principal component regression is applied to diverse input combinations, both with and without lagged streamflow (Q), resulting in twenty-three and twenty input combinations, respectively. Results demonstrate the critical role of including lagged Q for improved model accuracy, as models without lagged Q exhibit significant performance degradation. The second stage involves a comparative analysis of the MARS-Kmeans model with other machine-learning models, utilizing the best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms other models, showcasing superior accuracy in predicting monthly streamflow.

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On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran

Cited by 10 publications

References 80 publications

Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction

Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data

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