Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods by Wang Jie, Wang Guoqing, Elmahdi Amgad, Bao Zhenxin, Yang Qinli, Shu Zhangkang, Song Mingming

“…Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. …”
Get full text

An ensemble of daily simulated runoff data (1981–2099) under climate change conditions for 93 catchments in Switzerland (Hydro‐CH2018‐Runoff ensemble) by Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, Olivia Martius

“…Abstract We present a new ensemble of daily runoff simulations for meso‐scale catchments in Switzerland for the period 1981–2099: The Hydro‐CH2018‐Runoff ensemble. …”
Get full text

Operational hydrological data assimilation with the recursive ensemble Kalman filter by H. K. McMillan, E. Ö. Hreinsson, M. P. Clark, S. K. Singh, C. Zammit, M. J. Uddstrom

“…The forecast system performance was correspondingly improved in terms of Nash–Sutcliffe score, persistence index and bounding of the measured flow by the model ensemble. …”
Get full text

Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches by Geetabai S. Hukkeri, Sujay Raghavendra Naganna, Dayananda Pruthviraja, Nagaraj Bhat, R. H. Goudar

“…The root mean square error and normalized Nash-Sutcliffe efficiency ranges of the multivariate adaptive regression splines model (during test phase) were 0.37–0.54 and 0.78–0.87, respectively. …”
Get full text

River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method by H. Sanikhani, Y. Dinpashoh, M. A. Ghorbani, M. Zarghami

“…Nash–Sutcliffe model efficiency coefficient (E) for nearest neighbor probabilistic ensemble method in Dizaj and Mashin Stations during test period of model obtained 0.91 and 0.93, respectively.The investigation on the performance of models in different basins showed that the models have better performance in Zard river basin compared to Baranduz-Chaybasin. …”
Get full text

Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements by Kieu Anh Nguyen, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang

“…It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). …”
Get full text

Two-level deep learning ensemble model for forecasting hydroelectricity production by Njogho Kenneth Tebong, Théophile Simo, Armand Nzeukou Takougang

“…The LSTM model was the best model among the individual deep models with a Mean square error (MSE), Mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE) of 533.557 (MWh)2, 15.690 MWh, and 0.646 respectively while the proposed two-level ensemble model had an MSE of 448.711 (MWh)2, MAE of 14.938 MWh, and NSE of 0.702 respectively.…”
Get full text

Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration by Sani Isah Abba, Nguyen Thi Thuy Linh, Jazuli Abdullahi, Shaban Ismael Albrka Ali, Quoc Bao Pham, Rabiu Aliyu Abdulkadir, Romulus Costache, Van Thai Nam, Duong Tran Anh

“…Afterwards, the first scenario used four different ensemble techniques (ET). Two linear, i.e. simple averaging ensemble (SAE) and weighted averaging ensemble (WAE) and two nonlinear namely; backpropagation neural network ensemble (BPNN-E) and HW ensemble (HW-E). …”
Get full text

MODELLING THE PM2.5 CONCENTRATION WITH ARTIFICIAL INTELLIGENCE-BASED ENSEMBLE APPROACH by Mukhtar Nuhu Yahya, İbrahim Khalil Umar

“…The models were evaluated using 4 performance criteria including Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Correlation Coefficient (CC) and Bias (BIAS). …”
Get full text

Ensemble learning of daily river discharge modeling for two watersheds with different climates by Jingwen Xu, Qun Zhang, Shuang Liu, Shaojie Zhang, Shengjie Jin, Dongyu Li, Xiaobo Wu, Xiaojing Liu, Ting Li, Hao Li

“…Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. …”
Get full text

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction by Ali Omran Al-Sulttani, Mustafa Al-Mukhtar, Ali B. Roomi, Aitazaz Ahsan Farooque, Khaled Mohamed Khedher, Zaher Mundher Yaseen

“…The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R²), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). …”
Get full text

Enhanced soft computing for ensemble approach to estimate the compressive strength of high strength concrete by Munish KUMAR, Parveen SIHAG, Varun SINGH

Get full text

Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment by J.-F. Exbrayat, N. R. Viney, J. Seibert, S. Wrede, H.-G. Frede, L. Breuer

“…<br><br> Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. …”
Get full text

A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting by Mingshen Lu, Qinyao Hou, Shujing Qin, Lihao Zhou, Dong Hua, Xiaoxia Wang, Lei Cheng

“…The results show that the proposed model outperforms the base models and other ensemble models in terms of prediction accuracy. Compared with the XGB and weighted averaging ensemble (WAE) models, the proposed model has a 10.22% and 8.54% increase in Nash–Sutcliffe efficiency (NSE), an 18.52% and 16.38% reduction in root mean square error (RMSE), a 28.17% and 18.66% reduction in mean absolute error (MAE), and a 4.54% and 4.19% increase in correlation coefficient (<i>r</i>). …”
Get full text

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations by W. Qi, C. Zhang, G. Fu, C. Sweetapple, H. Zhou

“…Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash&ndash;Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. …”
Get full text

Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin by Xinchi Chen, Xiaohong Chen, Dong Huang, Huamei Liu

“…Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. …”
Get full text

Ensemble flood simulation for the typical catchment in humid climatic zone by using multiple hydrological models by J. Wang, J. Wang, J. Wang, J. Zhang, J. Zhang, G. Wang, G. Wang, X. Song, X. Song, X. Song, X. Yang, X. Yang, Y. Wang, Y. Wang

“…The performance of the ensemble members and multi-model ensemble simulation method was evaluated by comparing indicators of Nash-Efficiency coefficient, errors in root mean square, peak occurrence time, and relative errors of flood peak discharge, event runoff depth. …”
Get full text

Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning by Yuxiu Liu, Xing Yuan, Yang Jiao, Peng Ji, Chaoqun Li, Xindai An

“…The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. …”
Get full text

Using Ensemble-Based Systems with Near-Infrared Hyperspectral Data to Estimate Seasonal Snowpack Density by Mohamed Karim El Oufir, Karem Chokmani, Anas El Alem, Monique Bernier

“…The performance of the EBS was validated using an independent database and the results were satisfactory (R² = 0.90, RMSE = 44.45 kg m⁻³, BIAS = 3.87 kg m⁻³ and NASH = 0.89).…”
Get full text

STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production by Njogho Kenneth Tebong, Théophile Simo, Armand Nzeukou Takougang, Patrick Herve Ntanguen

“…Evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Nash Sutcliff Efficiency (NSE), were applied to assess model performance. …”
Get full text