12 citations as recorded by crossref.

1. Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review A. Ruiz Hurtado et al. https://doi.org/10.15446/ing.investig.108609
2. Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags M. Gocić & M. Arab Amiri https://doi.org/10.1007/s11269-021-02820-8
3. Imputação de dados climáticos utilizando a decomposição por valores singulares: uma comparação empírica M. García-Peña et al. https://doi.org/10.1590/0102-778620130005
4. Application of ANN and ANFIS models for reconstructing missing flow data M. Dastorani et al. https://doi.org/10.1007/s10661-009-1012-8
5. Application of ANN and ANFIS Models on Dryland Precipitation Prediction (Case Study: Yazd in Central Iran) M. Dastorani et al. https://doi.org/10.3923/jas.2010.2387.2394
6. Forecasting natural gas spot prices with nonlinear modeling using Gamma test analysis N. Salehnia et al. https://doi.org/10.1016/j.jngse.2013.07.002
7. Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors N. Gazzaz et al. https://doi.org/10.1016/j.marpolbul.2012.08.005
8. Artificial Neural Network (ANN)-Based Water Quality Index (WQI) for Assessing Spatiotemporal Trends in Surface Water Quality—A Case Study of South African River Basins T. Banda & M. Kumarasamy https://doi.org/10.3390/w16111485
9. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation V. Nourani et al. https://doi.org/10.1016/j.engappai.2008.09.003
10. Comparison of missing value imputation methods in time series: the case of Turkish meteorological data C. Yozgatligil et al. https://doi.org/10.1007/s00704-012-0723-x
11. Spatial and temporal monthly precipitation forecasting using wavelet transform and neural networks, Qara-Qum catchment, Iran M. Arab Amiri et al. https://doi.org/10.1007/s12517-016-2446-2
12. Testing Backpropagation Neural Network Approach in Interpolating Missing Daily Precipitation T. Gao & H. Wang https://doi.org/10.1007/s11270-017-3584-1