Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach

Azad, Sarita; Debnath, Shoubhik; Rajeevan, M.

doi:10.1007/s40710-015-0108-0

Cited by 21 publications

(7 citation statements)

References 23 publications

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“…Wavelet analysis has been used in different parts of the world to identify the periodicity in hydroclimatic time series data (Smith et al, 1998;Azad et al, 2015;Nalley et al, 2012;Araghi et al, 2015;Pathak et al, 2016). WT, a multiresolution analytical approach, can be applied to analyze time series data because it offers flexible window functions that can be changed over time (Nievergelt, 2001;Percival and Walden, 2000).…”

Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Rahman

Ahmed

Adnan

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The objectives of the present study were to explore the changes in the water balance components (WBCs) by co-utilizing the discrete wavelet transform (DWT) and different forms of the Mann-Kendall (MK) test and develop a wavelet denoise autoregressive integrated moving average (WD-ARIMA) model for forecasting the WBCs. The results revealed that most of the potential evapotranspiration (P ET ) trends (approximately 73 %) had a decreasing tendency from 1981-1982 to 2012-2013 in the western part of Bangladesh. However, most of the trends (approximately 82 %) were not statistically significant at a 5 % significance level. The actual evapotranspiration (A ET ), annual deficit, and annual surplus also exhibited a similar tendency. The rainfall and temperature exhibited increasing trends. However, the WBCs exhibited an inverse trend, which suggested that the P ET changes associated with temperature changes could not explain the change in the WBCs. Moreover, the 8-year (D3) and 16-year (D4) periodic components were generally responsible for the trends found in the original WBC data for the study area. The actual data was affected by noise, which resulted in the ARIMA model exhibiting an unsatisfactory performance. Therefore, wavelet denoising of the WBC time series was conducted to improve the performance of the ARIMA model. The quality of the denoising time series data was ensured using relevant statistical analysis. The performance of the WD-ARIMA model was assessed using the Nash-Sutcliffe efficiency (NSE) coefficient and coefficient of determination (R 2 ). The WD-ARIMA model exhibited very good performance, which clearly demonstrated the advantages of denoising the time series data for forecasting the WBCs. The validation results of the model revealed that the forecasted values were very close to actual values, with an acceptable mean percentage error. The residuals also followed a normal distribution. The performance and validation results indicated that models can be used for the short-term forecasting of WBCs. Further studies on different combinations of wavelet analysis are required to develop a superior model for the hydrological forecasting in the context of climate change. The findings of this study can be used to improve water resource management in the highly irrigated western part of Bangladesh.

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Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Rahman

Ahmed

Adnan

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

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Anonymous

2018

Preprint

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“…Typical applications include the following, among many others: predicting the dispersion coefficient (D) in a river ecosystem (Antonopoulos et al 2015); modelling the permeability losses in permeable reactive barriers (Santisukkasaem et al 2015); estimating the reference evapotranspiration (ET 0 ) in India (Adamala et al 2015); calculating the dynamic coefficient in porous media ; predicting Indian monsoon rainfall (Azad et al 2015); modeling of arsenic (III) removal (Mandal et al 2015); predicting effluent biochemical oxygen demand (BOD) in a wastewater treatment plant (Heddam et al 2016); modeling Secchi disk depth (SD) in river (Heddam 2016a); and predicting phycocyanin (PC) pigment concentration in river (Heddam 2016b). Unsurprisingly, regarding the high capabilities of ANNs in developing environmental models, they have rapidly gained much popularity.…”

Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Heddam

2016

Environ. Process.

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This study presents a new method called optimally pruned extreme learning machine (OP-ELM) for forecasting dissolved oxygen concentration (DO) several hours in advance. The forecast time horizon ranges from 24-h ahead (one day) to 168-h ahead (seven days). The proposed OP-ELM model is compared to the standard multilayer perceptron neural network (MLPNN) with respect to their capabilities of forecasting DO in the Klamath River at Miller Island Boat Ramp, Oregon, USA. To demonstrate the forecasting capability of OP-ELM and MLPNN models, we used a long-term data set of hourly DO data for a ten-year period, from 1 January 2004 to 31 December 2013, collected by the United States Geological Survey (USGS Stations No: 420,853,121,505,500 [Top] and 420,853,121,505,501 [Bottom]). For developing the models, we split the data set into a training subset (from 2004 to 2010) that corresponded to 70 %, and a validation (from 2011 to 2013) that corresponded to 30 % of the total data set. We investigated the performance and accuracy of the proposed two models for three different horizons, i.e., short-term, medium-term and long-term forecasting; a total of six different models (FM1 to FM6), having the same data sets as inputs, were developed for short-term (24 h to 48 h), medium-term (72 h to 96 h) and long-term (120 h to 168 h) horizons. Input variables used in the six models were the six antecedent DO concentrations at (t-5), (t-4), (t-3), (t-2), (t-1) and (t). The performance of the OP-ELM and MLPNN models in training and validation sets were compared with the observed data. To get more accurate evaluation of the results of the two models, the following seven statistical performance indices were used: the coefficient of correlation (R), the Willmott index of agreement (d), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), the mean absolute error (MAE), the bias error (Bias), and the mean absolute percentage error (MAPE). The study reveals that OP-ELM and MLPN provided good results and they were successful in forecasting DO at a high level of accuracy. The reliability of forecasting decreased Environ. Process. with increasing the step ahead. The measures of model performance fell within the acceptable ranges for the two stations. Regarding the fact that researches on medium and long-term forecasting are relatively limited, the present work aims to build and provide a good early warning system capable of preventing DO depletion and the associated problems of anoxia and hypoxia in river. Furthermore, the proposed forecasting models, when implemented appropriately, could be reliably used in detecting future change in DO concentration in rivers.

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Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach

Cited by 21 publications

References 23 publications

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Reply to interactive comments of Referee #2

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

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