Abstract
In this research, k-means, agglomerative hierarchical clustering and regression analysis have been applied in hydrological real time series in the form of patterns and models, which gives the fruitful results of data analysis, pattern discovery and forecasting of hydrological runoff of the catchment. The present study compares with the actual field data, predicted value and validation of statistical yields obtained from cluster analysis, regression analysis with ARIMA model. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models is investigated for monthly runoff forecasting. The different parameters have been analyzed for the validation of results with casual effects. The comparison of model results obtained by K-means & AHC have very close similarities. Result of models is compared with casual effects in the same scenario and it is found that the developed model is more suitable for the runoff forecasting. The average value of R2 determined is 0.92 for eight ARIMA models. This shows more accuracy of developed ARIMA model under these processes. The developed rainfall runoff models are highly useful for water resources planning and development.
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Agarwal, P., Alam Afshar, M. and Biswas, R. (2010) Analysing the agglomerative hierarchical clustering algorithm for categorical attributes. IJJMT, v.1(2), pp.186–190.
Boudaghpour, S., Bagheri, M. and Bagheri, Z. (2014) Using Stochastic Modeling Techniques to Predict the Changes of Total Suspended Solids and Sediments in Lighvan Chai Catchment Area in Iran. Jour. River Engg., v.2(1), pp.1–8.
Chakraborty, S., Denis, D.M. and Sherring, A. (2013) Development of Time Series Autoregressive Model for prediction of rainfall and runoff in Kelo Watershed Chhattisgarh. Internat. Jour. Advan. Engg. Sci. Tech., v.2, pp.153–163.
Cohen, L. (1995) Time-Frequency Analysis. Prentice Hall, Eaglewood Cliff, NJ.
Han, J.W. and Kamber, M. (2001) Data Mining Concept and techniques, Morgan Kaufman: San Francisco, CA.
Laxman, S. and Sastry, P.S. (2006) A survey of temporal data mining. Sadhana, v.31(2), pp.173–198.
Makridakis, S. and Hison, M. (1995) ARMA Models and The Box Jenkins methodology by revised version of 95/33/TM. pp.1–17.
Mohammadi, K., Eslami, R.H. and Kahawita, R. (2006) Parameter estimation of an ARMA model for river flow forecasting using Goal programming. Elsevier Sciences, Jour. Hydrol., v.33, pp.1293–299.
Mishra, S., Sarvanan, C., Dwivedi, V.K., and Pathak, K.K. (2014) Discovering flood recession pattern in hydrological time series data mining during the post monsoon period. IJCA, v.90(08), pp.35–44.
Mishra, S., Sarvanan, C., Dwivedi, V.K. and Pathak, K.K. (2015) Discovering flood rising pattern in hydrological time series data mining during the pre-monsoon period. Indian Jour. Geo-Marine Sci., v.44(3), pp.303–317.
Mishra, S., Dwivedi, V.K., Sarvanan, C. and Pathak, K.K. (2013) Pattern discovery in hydrological time series data mining during the monsoon period of the high flood years in Brahmaputra river basin. IJCA, v.67(6), pp.7–14.
Mishra, S., Saravanan, C. and Dwivedi, V.K. (2015) Study of time series data mining for the real time hydrological forecasting–A review. IJCA, v.67(6) pp.7–14.
Mishra, S., Sarvanan, C., Dwivedi, V.K. and Shukla, J. P. (2014) Rainfall prediction using hydrological time series data mining. Published in national workshop on Technologies for Sustainable Rural Development-Having Potential for Socio-economic Upliftment during July, 04–05 (2014), at CSIR-AMPRI, Bhopal, pp.403–408.
Mishra, S., Chaubey, V., Pandey, S.K. and Shukla, J.P., (2014) An efficient approach of support vector machine in runoff forecasting. IJSER, v.5(3), pp.158–167.
Mishra, S., Gupta, P., Pandey, S.K. and Shukla, J.P. (2014) An efficient approach of artificial neural network in runoff forecasting. IJCA, v.92(5), pp.9–15.
Morales, K.H., Ibrahim J.G., Chen C.J. and Ryan. L.M. (2006) Bayesian Model averaging with applications to benchmark dose estimation for arsenic in drinking water. Jour. Amer. Statistical Assoc., v.101(473), pp.9–17.
Mujumdar, P.P. and Kumar, D.N. (1990) Stochastic models of stream flow: some case studies. Hydrol. Sci. Jour., v.35(4), pp.395–410.
Naill, P.E. and Momani M. (2009) Time series analysis model for rainfall data Jordan: case study for using time series analysis. Amer. Jour Environ. Sci., v.5(5) pp.599–604.
Nasseria, M. and Zahraiea, B. (2011) Application of simple clustering on spacetime mapping of mean monthly rainfall pattern. Internat. Jour. Climatol., v.31,pp.732–741.
Nigam, R., Nigam, S. and Mittal, S.K. (2014) The river runoff forecast based on the modeling of time series. Russ. Meteorol. Hydrol., v.11, pp.56–73.
Ouyang, R., Ren, L., Cheng, W. and Zhou, C. (2010) Similarity search and pattern discovery in Hydrological time series data mining. Wiley InterScience, Hydrol. Process, v.24, pp.1198–1210.
Oyebode, E.O., Adekalu, K.O. and Fashoto, S.G. (2010) Development of rainfall-runoff forecast model. IJEMI, v.1(1-3), pp.56–66.
Piatetsky-Shapiro, G. and Frawley, W.J. (1991) Knowledge Discovery in Databases. AAAI/MIT Press: Boston, MA.
Purviya, R., Tiwari, H.L. and Mishra, S., (2014) Application of clustering data mining techniques in temporal data sets of hydrology: a review. IJSET, v.3(4), pp.360–365.
Salas, J.D., Sveinsson, O.G., Lane, W.L. and Frevert, D.K. (2006) Stochastic Streamflow Simulation Using SAMS-2003. Jour. Irrigation and Drainage Engg., v.133, pp.112–122.
Sykes, A.O. (1992) Regression.pdfý (www.law.uchicago.edu/files/files/20.Sykes.Regression.pdfý), pp.1–33.
Tesfaye, Y.G., Meerschaert, M.M. and Andersonet, P.L., (2006) Identification of periodic autoregressive moving average models and their application to the modelling of river flows. Water Resources Res., v.42, pp.1–11.
Toth, E., Brath, A. and Montanari, A. (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. Jour. Hydrol., v.239, pp.132–147.
Valipour, M. (2015) Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorological Applications, v.22(3), pp.592–598.
Wang, C.W., Chau, W.K., Cheng, T.C. and Qiu, L. (2009) A comparison of performance of several artificial intelligent methods for forecasting monthly discharge time series. Jour. Hydrol., v.374(3-4), pp.294–306.
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Mishra, S., Saravanan, C., Dwivedi, V.K. et al. Rainfall-Runoff Modeling using Clustering and Regression Analysis for the River Brahmaputra Basin. J Geol Soc India 92, 305–312 (2018). https://doi.org/10.1007/s12594-018-1012-9
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DOI: https://doi.org/10.1007/s12594-018-1012-9