30 citations as recorded by crossref.

1. Deterministic chaos and historical geomorphology: A review and look forward J. Phillips https://doi.org/10.1016/j.geomorph.2005.10.004
2. A probe into the chaotic nature of total ozone time series by correlation dimension method G. Chattopadhyay & S. Chattopadhyay https://doi.org/10.1007/s00500-007-0267-7
3. Simulating rainfall time-series: how to account for statistical variability at multiple scales? F. Oriani et al. https://doi.org/10.1007/s00477-017-1414-z
4. Statistical analysis of precipitation variations and its forecasting in Southeast Asia using remote sensing images A. Syed et al. https://doi.org/10.3389/fenvs.2022.832427
5. Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge M. Zounemat-Kermani https://doi.org/10.1007/s11269-016-1258-1
6. Analyzing surface air temperature and rainfall in univariate framework, quantifying uncertainty through Shannon entropy and prediction through artificial neural network S. Ray & S. Chattopadhyay https://doi.org/10.1007/s12145-020-00555-5
7. Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data S. Khan et al. https://doi.org/10.1103/PhysRevE.76.026209
8. Hydrologic complexity and classification: a simple data reconstruction approach B. Sivakumar et al. https://doi.org/10.1002/hyp.6362
9. Statistical moments of Gaussian kernel correlation sum and weighted least square estimator of correlation dimension and noise level Z. Dhifaoui https://doi.org/10.1016/j.jspi.2017.08.001
10. Correlation dimension estimation of hydrological series and data size requirement: myth and reality/Estimation de la dimension de corrélation de séries hydrologiques et taille nécessaire du jeu de données: mythe et réalité B. Sivakumar https://doi.org/10.1623/hysj.2005.50.4.591
11. Long-Term Stochastic Modeling of Monthly Streamflow in River Nile S. Abdelaziz et al. https://doi.org/10.3390/su15032170
12. Streamflow Modelling: A Primer on Applications, Approaches and Challenges D. Bourdin et al. https://doi.org/10.1080/07055900.2012.734276
13. Nonlinear statistics reveals stronger ties between ENSO and the tropical hydrological cycle S. Khan et al. https://doi.org/10.1029/2006GL027941
14. Relationship between fractal dimensions of stream and morphometric characteristics of basin for the soil conservation from water erosion S. Mofidi et al. https://doi.org/10.1080/15715124.2021.1981353
15. Detecting chaos in time series D. Gerasimov & S. Morgunova https://doi.org/10.1088/1742-6596/1683/2/022014
16. Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent D. Mihailović et al. https://doi.org/10.1016/j.physa.2019.03.041
17. Root mean square error or mean absolute error? Use their ratio as well D. Karunasingha https://doi.org/10.1016/j.ins.2021.11.036
18. Fourier spectral heuristic indices for streamflow complexity and predictability F. Frau et al. https://doi.org/10.1016/j.jhydrol.2026.136483
19. Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach S. Chattopadhyay et al. https://doi.org/10.1002/met.211
20. The complex future of hydrogeology D. Galloway https://doi.org/10.1007/s10040-010-0585-1
21. Measuring nonlinear dependence in hydrologic time series H. Kim et al. https://doi.org/10.1007/s00477-008-0268-9
22. NONLINEAR DETERMINISTIC ANALYSIS OF AIR POLLUTION DYNAMICS IN A RURAL AND AGRICULTURAL SETTING B. SIVAKUMAR et al. https://doi.org/10.1142/S0219525907001288
23. Nonlinear dynamics of stream nutrient concentrations: low dimensionality, structured complexity, and seasonal predictability horizon W. Bin Mamoon et al. https://doi.org/10.1016/j.jhydrol.2026.135432
24. Nonlinear determinism in river flow: prediction as a possible indicator B. Sivakumar https://doi.org/10.1002/esp.1462
25. Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction Y. Jiang et al. https://doi.org/10.1007/s11269-020-02631-3
26. Comparative study among different neural net learning algorithms applied to rainfall time series S. Chattopadhyay & G. Chattopadhyay https://doi.org/10.1002/met.71
27. Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward B. Sivakumar https://doi.org/10.1007/s00477-008-0265-z
28. Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN S. Chattopadhyay & G. Chattopadhyay https://doi.org/10.1016/j.crte.2009.10.016
29. Chaos theory applied to the modelling of karst springs: first results from univariate time series S. Mangiarotti et al. https://doi.org/10.1007/s10040-019-01971-8
30. Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction S. Meshram et al. https://doi.org/10.1007/s11269-020-02672-8