14 citations as recorded by crossref.

1. Deriving Reservoir Refill Operating Rules by Using the Proposed DPNS Model P. Liu et al. 10.1007/s11269-006-0322-7
2. Prediction of creep index of soft clays using gene expression programming X. Xue & C. Deng 10.1007/s00500-023-08053-8
3. River Flow Forecasting Using Neural Networks and Auto-Calibrated NAM Model with Shuffled Complex Evolution M. Zakermoshf et al. 10.3923/jas.2008.1487.1494
4. Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques Y. Wang et al. 10.3390/w7084144
5. Three-and-six-month-before forecast of water resources in a karst aquifer in the Terminio massif (Southern Italy) S. Rampone 10.1016/j.asoc.2013.05.016
6. Enhancing MIKE11 Updating Kernel and Evaluating Its Performance Using Numerical Experiments M. Saddagh & M. Abedini 10.1061/(ASCE)HE.1943-5584.0000427
7. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al. 10.5194/hess-16-1151-2012
8. A Brief review of flood forecasting techniques and their applications S. Jain et al. 10.1080/15715124.2017.1411920
9. A nonlinear perturbation model based on artificial neural network B. Pang et al. 10.1016/j.jhydrol.2006.09.015
10. A novel model for prediction of uniaxial compressive strength of rocks X. Xue 10.5802/crmeca.109
11. Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed P. Jeevaragagam & S. Simonovic 10.2478/johh-2023-0019
12. A dynamic artificial neural network for tomato yield prediction R. Salazar et al. 10.17660/ActaHortic.2017.1154.11
13. AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts X. Zha et al. 10.1061/(ASCE)HE.1943-5584.0001955
14. Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting Z. Liu et al. 10.1007/s11269-016-1275-0