scholarly journals Long-term trend analysis of rainfall using hybrid Discrete Wavelet Transform (DWT) based Mann-Kendall tests in central Gujarat region, India

MAUSAM ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 209-224
Author(s):  
RAJANI NIRAV V ◽  
TIWARI MUKESH K ◽  
CHINCHORKAR S S
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Trend Analysis ◽  
Time Scales ◽  
Series Data ◽  
Trend Test ◽  
Discrete Wavelet ◽  
Rainfall Time Series

Trend analysis has become one of the most important issues in hydro-meteorological variables study due to climate change and the focus given to it in the recent past from the scientific community. In this study, long-term trends of rainfall are analyzed in eight stations located in semi-arid central Gujarat region, India by considering time series data of 116 years (1901-2016). Discrete wavelet transform (DWT) as a dyadic arrangement of continuous wavelet transformation combined with the widely applied and acknowledged Mann-Kendall (MK) trend analysis method were applied for analysis of trend and dominant periodicities in rainfall time series at monthly, annual and monsoonal time scales. Initially, rainfall time series applied in this study were decomposed using DWT to generate sub-time series at high and low frequencies, before applying the MK trend test. Further, the Sequential Mann-Kendall (SQMK) test was also applied to find out the trend changing points. The result showed that at the monthly annual and monsoon time scales, the trends in rainfall were significantly decreasing in most of the station. The 4-month and 8-month components were found as dominant at the monthly time series and the 2-year and 4-year component were found as dominant at the monsoon time series, whereas the 2-year components were observed as dominant in the annual time scale.

scholarly journals Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transform and deep learning

2021 ◽  
Author(s):  
Xueyi You ◽  
Ming Wei
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Monthly Rainfall ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Efficiency Coefficient ◽  
Rainfall Forecasting

Actual rainfall forecast is critical to the management and allocation of water resources. In recent years, deep learning has been proved to be superior to traditional forecasting methods when predicting rainfall time series with high temporal and spatial variability. In this study, the discrete wavelet transform (DWT) and two typical deep learning approaches, namely long-short term memory (LSTM) and dilated causal convolutional neural network (DCCNN), are integrated innovatively and the hybrid model (DWT-CLSTM-DCCNN) is used for monthly rainfall forecasting for the first time. Monthly rainfall time series of four major cities in China (Beijing, Tianjin, Chongqing and Guangzhou) are used as the dataset of DWT-CLSTM-DCCNN. Firstly, two methods of sample construction are used to train DWT-CLSTM-DCCNN and their effects on the model performance are analyzed. Then, LSTM and the dilated causal convolutional network (DCCNN) are established as the benchmark models, and their forecast accuracy is compared with that of DWT-CLSTM-DCCNN. From the results of the evaluation criteria such as mean absolute error (MAE), root mean squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE) as well as the fitting curve for forecasted rainfall, it can be concluded that the DWT-CLSTM-DCCNN developed in this study outperforms the benchmark models in model accuracy, peak and mutational rainfall capturing ability. Compared with the previous studies, DWT-CLSTM-DCCNN is proven to be better peak capture and more suitable for long-term rainfall forecasting.

scholarly journals Drought Trend Analysis in Kano Using Standardized Precipitation Index (SPI)

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Ifeanyi Chukwudi Achugbu ◽  
Stanley Anugwo
Keyword(s):  
Trend Analysis ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Series Data ◽  
Rainfall Time Series ◽  
Entire Study ◽  
Significant Downward Trend ◽  
Increasing Trend

The trend analysis was carried out using non-parametric Mann-Kendall trend test for Kano using a long term 100 years rainfall data. In other to assess the short term, seasonal, annual and long term droughts, the study employed the Standardized Precipitation Index (SPI) to 3, 6, 9, 12 and 24 month time scales using the rainfall time series data. The SPI values computed for all the time scales revealed a non significant increasing trend for the entire study period (1911-2010), while period 1911-1995 revealed a significant decreasing trend especially in August, September and October. For comparison between different time periods, the 100 years series was sub-divided into 30 years overlapping time period. Period 1951-1980 and 1961-1990 revealed the highest number of statistically significant downward trend. The Z values from Mann-Kendall test ranges from 4.05 to -2.86, which shows how erratic the rainfall could be in Kano. All the analyzed months for periods 1911-1940, 1971-2000, 1981-2010 and 1941-1970 (except May in 1941-1970) showed a general increasing trend for all the time scales. However, periods 1971-2000 and 1981-2010 showed a significant increasing trend which implies that rainfall over the station is at the increase. The value of the slope ranges between -0.053 and 0.118 for all the time scales. High slope values were more prevalent in the higher time scales.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS

2004 ◽  
Vol 02 (04) ◽  
pp. 567-587 ◽  
Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Wavelet Filters ◽  
The Hilbert Transform ◽  
High Pass

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

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