Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

Chattopadhyay, Surajit; Chattopadhyay, Goutami

doi:10.1016/j.crte.2009.10.016

Cited by 86 publications

(42 citation statements)

References 65 publications

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“…In general, the foregoing results bear comparison with results of some early studies (e.g. Chattopadhyay and Chattopadhyay, 2010;Chattopadhyay et al, 2011), which confirmed that ARIMA forecasting models are suitable for hydrological data. Furthermore, detailed studies on ARIMA and artificial neural network (ANN) modelling (e.g.…”

Section: Arima Modellingsupporting

confidence: 81%

Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling

Razak

Aris

Ramli

et al. 2016

J Flood Risk Management

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Accurate and efficient flood forecasting system can improve the emergency rescue plans and help avoid the loss of lives. This study aims to identify the trends in rainfall and streamflow in Segamat River (Malaysia) by using Mann–Kendall trend analysis, to develop time series flood forecasting model by the application of autoregressive integrated moving average (ARIMA) modelling approach. The accuracy of the optimal ARIMA model was verified by Spearman's rank correlation and linear regression analysis. The best ARIMA model was ARIMA (0, 1, 2). Trend analysis indicates that there was a trend of significant increase in rainfall rates at Kemelah Station and significant decrease at the Bandar Segamat Station, whereas streamflows at Bandar Segamat showed a trend of significant decrease. There was also a trend of decrease in streamflow over the study period. The applications of statistical modelling are beneficial to relevant authorities in understanding the flood patterns, trends and their potential risk.

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Section: Arima Modellingsupporting

confidence: 81%

Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling

Razak

Aris

Ramli

et al. 2016

J Flood Risk Management

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“…This study investigated the applicability of the adaptive neuro-fuzzy inference system (ANFIS) and the autoregressive integrated moving average (ARIMA) models in water-level modeling. Results showed approximately similar precision to that of NN models; the same results have been reported by Chattopadhyay and Chattopadhyay (2010). The main distinction between the NN and ARIMA models is the required level of user input (human expertise) and model-building speed, with NN model building reported as being faster than ARIMA model building.…”

supporting

confidence: 78%

Klang River–Level Forecasting Using ARIMA and ANFIS Models

et al. 2013

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Selection of the right modeling technique is always a challenging issue because every model can produce only an approximation of the reality it is attempting to illustrate. As a result, model performance in a specific situation is the only criterion that confirms the model's applicability in that particular situation. This study investigated the applicability of the adaptive neuro‐fuzzy inference system (ANFIS) and the autoregressive integrated moving average (ARIMA) models in water‐level modeling. Results showed a definite preference for the ANFIS model against the simple‐ARIMA model, but an updated‐ARIMA model outperformed ANFIS. A mean absolute error of < 1% in each model confirmed the applicability of these models in predicting the water level in the Klang River in Malaysia. On the basis of the obtained prediction accuracy level, the updated‐ARIMA and ANFIS models are introduced as reliable and accurate models for prompt decision‐making, planning, and urgent managing of water resources in crisis.

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“…In this context, the average surface temperature of our planet is mainly controlled by its albedo and the atmospheric greenhouse effect (Cracknell and Varotsos, 1994, 2007aVarotsos, 2002Varotsos, , 2005aFeretis et al, 2002;Efstathiou et al, 2003; Published by Copernicus Publications on behalf of the European Geosciences Union. Tzanis et al, 2008;Varotsos et al, 2008Varotsos et al, , 2012aChattopadhyay and Chattopadhyay, 2010;Varotsos, 2010, 2013;Xue et al, 2011;de la Fuente et al, 2011). For example, the average Earth surface temperature would be −40 • C if it were frozen entirely and 27 • C if all the ice on its surface were to melt.…”

Section: Introductionmentioning

confidence: 99%

New spectral functions of the near-ground albedo derived from aircraft diffraction spectrometer observations

et al. 2014

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Abstract. The airborne spectral observations of the upward and downward irradiances are revisited to investigate the dependence of the near-ground albedo as a function of wavelength in the entire solar spectrum for different surfaces (sand, water, snow) and under different conditions (clear or cloudy sky). The radiative upward and downward fluxes were determined by a diffraction spectrometer flown on a research aircraft that was performing multiple flight paths near the ground. The results obtained show that the near-ground albedo does not generally increase with increasing wavelengths for all kinds of surfaces as is widely believed today. Particularly, in the case of water surfaces it was found that the albedo in the ultraviolet region is more or less independent of the wavelength on a long-term basis. Interestingly, in the visible and near-infrared spectra the water albedo obeys an almost constant power-law relationship with wavelength. In the case of sand surfaces it was found that the sand albedo is a quadratic function of wavelength, which becomes more accurate if the ultraviolet wavelengths are neglected. Finally, it was found that the spectral dependence of snow albedo behaves similarly to that of water, i.e. both decrease from the ultraviolet to the near-infrared wavelengths by 20-50 %, despite the fact that their values differ by one order of magnitude (water albedo being lower). In addition, the snow albedo vs. ultraviolet wavelength is almost constant, while in the visible near-infrared spectrum the best simulation is achieved by a second-order polynomial, as in the case of sand, but with opposite slopes.

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Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

Cited by 86 publications

References 65 publications

Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling

Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling

Klang River–Level Forecasting Using ARIMA and ANFIS Models

New spectral functions of the near-ground albedo derived from aircraft diffraction spectrometer observations

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