28 citations as recorded by crossref.

1. Entropy based assessment of hydrometric network using probabilistic approach N. Vivekanandan https://doi.org/10.26634/jste.2.3.2588
2. An entropy approach for the optimization of cross-section spacing for river modelling E. Ridolfi et al. https://doi.org/10.1080/02626667.2013.822640
3. Semivariance analysis and transinformation entropy for optimal redesigning of nutrients monitoring network in San Francisco bay A. Boroumand et al. https://doi.org/10.1016/j.marpolbul.2017.10.057
4. Spatial-temporal analysis of precipitation extremes over China during 1961–2019 S. Liu & G. Chang https://doi.org/10.1007/s44292-025-00049-3
5. Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts M. Eini et al. https://doi.org/10.1016/j.ijdrr.2020.101687
6. Seasonal precipitation variability in mainland China based on entropy theory G. Wu et al. https://doi.org/10.1002/joc.7128
7. Raingauge network optimization in a tropical urban area by coupling cross-validation with the geostatistical technique M. Ali & F. Othman https://doi.org/10.1080/02626667.2018.1437271
8. Ensemble Entropy for Monitoring Network Design L. Alfonso et al. https://doi.org/10.3390/e16031365
9. Information theory-based multi-objective design of rainfall network for streamflow simulation W. Wang et al. https://doi.org/10.1016/j.advwatres.2019.103476
10. Objective functions for information-theoretical monitoring network design: what is “optimal”? H. Foroozand & S. Weijs https://doi.org/10.5194/hess-25-831-2021
11. A new methodology to define homogeneous regions through an entropy based clustering method E. Ridolfi et al. https://doi.org/10.1016/j.advwatres.2016.07.007
12. Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy W. Wang et al. https://doi.org/10.1016/j.envres.2019.108686
13. State fusion entropy for continuous and site-specific analysis of landslide stability changing regularities Y. Liu et al. https://doi.org/10.5194/nhess-18-1187-2018
14. Optimal Rain Gauge Network Design Aided by Multi-Source Satellite Precipitation Observation H. Wang et al. https://doi.org/10.3390/rs14236142
15. An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data C. Bertini et al. https://doi.org/10.2166/nh.2021.113
16. Comparison and Analysis of Different Discrete Methods and Entropy-Based Methods in Rain Gauge Network Design Y. Huang et al. https://doi.org/10.3390/w11071357
17. Developing a dual entropy-transinformation criterion for hydrometric network optimization based on information theory and copulas H. Li et al. https://doi.org/10.1016/j.envres.2019.108813
18. Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis I. Mares et al. https://doi.org/10.3390/e24101375
19. Discrete entropy theory for optimal redesigning of salinity monitoring network in San Francisco bay A. Boroumand & T. Rajaee https://doi.org/10.2166/ws.2016.110
20. Evaluation and Selection of Rain Gauge Network using Entropy N. Vivekanandan & R. Jagtap https://doi.org/10.1007/s40030-013-0032-0
21. Entropy Applications to Water Monitoring Network Design: A Review J. Keum et al. https://doi.org/10.3390/e19110613
22. Rainfall Network Optimization Using Radar and Entropy H. Yeh et al. https://doi.org/10.3390/e19100553
23. Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework J. Chacon-Hurtado et al. https://doi.org/10.5194/hess-21-3071-2017
24. A Mutual Information Theory‐Based Approach for Assessing Uncertainties in Deterministic Multi‐Category Precipitation Forecasts Y. Ning et al. https://doi.org/10.1029/2022WR032631
25. Optimization of rainfall networks using information entropy and temporal variability analysis W. Wang et al. https://doi.org/10.1016/j.jhydrol.2018.02.010
26. Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB M. Norallahi & H. Seyed Kaboli https://doi.org/10.1007/s11069-020-04453-3
27. Comparative study of statistical methods to identify a predictor for discharge at Orsova in the Lower Danube Basin I. Mares et al. https://doi.org/10.1080/02626667.2019.1699244
28. Use of the entropy approach in water resource monitoring systems V. Bezsonnyi https://doi.org/10.26565/2410-7360-2023-58-23