12 citations as recorded by crossref.

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2. Improvement of the SWAT model for event-based flood simulation on a sub-daily timescale D. Yu et al. https://doi.org/10.5194/hess-22-5001-2018
3. Performance evaluation of machine learning-based classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia G. Antariksa et al. https://doi.org/10.1016/j.petrol.2021.109250
4. Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable? I. Malik et al. https://doi.org/10.1088/2752-5295/acfa1a
5. BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts J. Liu & Z. Xie https://doi.org/10.1175/MWR-D-13-00031.1
6. Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran S. Aminyavari et al. https://doi.org/10.1007/s00376-017-7082-6
7. Evaluation of machine learning methods for lithology classification using geophysical data T. Bressan et al. https://doi.org/10.1016/j.cageo.2020.104475
8. Forecasting Monthly Rainfall using Bio-Inspired Artificial Algae Deep Learning Network A. Kala & S. Vaidyanathan https://doi.org/10.1142/S0219477522500183
9. Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network Y. Zhong et al. https://doi.org/10.2166/nh.2018.177
10. BMA probability quantitative precipitation forecasting of land-falling typhoons in south-east China L. Zhao et al. https://doi.org/10.1007/s11707-019-0802-8
11. How reliable are TIGGE daily deterministic precipitation forecasts over different climate and topographic conditions of Iran? S. Amini et al. https://doi.org/10.1002/met.2013
12. Evaluation of ECMWF mid-range ensemble forecasts of precipitation for the Karun River basin M. Abedi et al. https://doi.org/10.1007/s00704-020-03160-0