14 citations as recorded by crossref.

1. Hybrid MLP-IDW approach based on nearest neighbor for spatial prediction A. Tavassoli et al. https://doi.org/10.1007/s00180-021-01186-0
2. Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach Y. Seo et al. https://doi.org/10.1007/s11269-015-0935-9
3. Artificial Intelligence for Integrated Water Resources Management in Taiwan 斐. 张 https://doi.org/10.12677/JWRR.2013.25045
4. Type-2 interval fuzzy rule-based systems in spatial analysis F. Martino & S. Sessa https://doi.org/10.1016/j.ins.2014.03.114
5. Estimation of dew point temperature using SVM and ELM for humid and semi-arid regions of India P. Deka et al. https://doi.org/10.1080/09715010.2017.1408037
6. Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan F. Chang et al. https://doi.org/10.1080/02626667.2013.775447
7. Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions J. Hernandez-Torres et al. https://doi.org/10.3390/app142210508
8. The Ability of Artificial Neural Networks in Learning Dependency of Spatial Data‎ A. Tavasoli et al. https://doi.org/10.52547/jsri.16.1.211
9. Machine learning assisted Kriging to capture spatial variability in petrophysical property modelling M. Mohammadpour et al. https://doi.org/10.1016/j.marpetgeo.2024.106967
10. Scenario-Based Real-Time Flood Prediction with Logistic Regression J. Lee & B. Kim https://doi.org/10.3390/w13091191
11. Bulanık Yapay Sinir Ağları ve Çok Katmanlı Yapay Sinir Ağları ile Günlük Buharlaşma Tahmini D. Yıldırım et al. https://doi.org/10.21657/topraksu.654778
12. Terrestrial laser scanning for structural inspection with Kriging interpolation T. Sanchez et al. https://doi.org/10.1080/15732479.2020.1861469
13. Comparison of Kriging and artificial neural network models for the prediction of spatial data A. Tavassoli et al. https://doi.org/10.1080/00949655.2021.1961140
14. A typology of different development and testing options for symbolic regression modelling of measured and calculated datasets D. Beriro et al. https://doi.org/10.1016/j.envsoft.2013.03.020