31 research outputs found
Development of Regional Flood Frequency Relationships for Gauged and Ungauged Catchments Using L-Moments
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Vedantic variations in the presence of Europe: establishing the Hindu dharma in late nineteenth century Bengal
No full textWe will offer in this essay an analytic overview of four texts from the second half of the nineteenth century which elaborated different variations on the Hindu dharma. These are Rajnarayan Basu’s Hindu Dharmera Sresthata (‘The Superiority of the Hindu dharma’, 1879), Bankim Chandra Chatterjee’s Dharmatattva (1888, ‘Principles of Dharma’), Bhudeb Mukhopadhyay’s Sāmājika Prabandha (‘Essays on Society’, 1892), and Chandranath Basu’s Hindutva (‘Hinduness’, 1892). These textual constructions of Hindu identity sometimes have different argumentative goals, employ different rhetorical strategies, and draw upon different scriptural resources. Notwithstanding these distinctive variations relating to aims, methods, and styles, they seek to engage Europe as the Other both as a conceptual toolbox whose instruments can be appropriated for reconfiguring the Hindu dharmaand as a dialectical foil which highlights the superiority of the Hindu dharma to its diverse critics. For Rajnarayan Basu, Chatterjee, Mukhopadhyay, and Chandranath Basu, the Hindu dharma is the hermeneutic site for evaluating specific aspects of European modernities and for re-asserting Vedic norms, ideas, and practices through a critical engagement with European modernities. These texts are multi-faceted vignettes into the Bengali Hindu appropriations of European concepts in late nineteenth century Bengal, and also the anxieties about the stability of the Hindu dharma in an age of rapid socio-cultural transformations
Flood Forecasting and Uncertainty Assessment Using Wavelet- and Bootstrap-Based Neural Networks
No full textAccurate and reliable forecasting of flood is inevitable for flood control planning and rehabilitation. There are several models available for flood forecasting, but as far as accuracy, reliability, and data scarcity are concerned, soft computing techniques (e.g., artificial neural networks) have been found to achieve the target. A wavelet-, bootstrap-, and neural-network-based framework (BWANN) is presented here for flood forecasting. Performance comparison of the proposed BWANN model is presented with wavelet-based ANN (WANN), wavelet-based MLR (WMLR), bootstrap- and wavelet-analysis-based multiple linear regression models (BWMLR), traditional ANN, and traditional multiple linear regression (MLR) models for flood forecasting. For development of WANN models, original time series data is decomposed using wavelet transformation, and wavelet sub-time series are considered to develop WANN model. A comparative analysis is carried out among different approaches of WANN model development using wavelet sub-time series. </jats:p
Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach
No full textA new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting
No full textA new hybrid model, the wavelet–bootstrap–ANN (WBANN), for daily discharge forecasting is proposed in this study. The study explores the potential of wavelet and bootstrapping techniques to develop an accurate and reliable ANN model. The performance of the WBANN model is also compared with three more models: traditional ANN, wavelet-based ANN (WANN) and bootstrap-based ANN (BANN). Input vectors are decomposed into discrete wavelet components (DWCs) using discrete wavelet transformation (DWT) and then appropriate DWCs sub-series are used as inputs to the ANN model to develop the WANN model. The BANN model is an ensemble of several ANNs built using bootstrap resamples of raw datasets, whereas the WBANN model is an ensemble of several ANNs built using bootstrap resamples of DWCs instead of raw datasets. The results showed that the hybrid models WBANN and WANN produced significantly better results than the traditional ANN and BANN, whereas the BANN model is found to be more reliable and consistent. The WBANN and WANN models simulated the peak discharges better than the ANN and BANN models, whereas the overall performance of WBANN, which uses the capabilities of both bootstrap and wavelet techniques, is found to be more accurate and reliable than the remaining three models.</jats:p
Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models
No full textWavelet based flood forecasting models are known to perform better than conventional models, yet the effect of the way wavelet components are combined to develop a model on the forecasting performance, is inadequately investigated. To demonstrate this, two types of wavelet- adaptive neuro-fuzzy inference system (WANFIS), i.e. WANFIS-split data model (WANFIS-SD) and WANFIS-modified time series model (WANFIS-MS) are developed to forecast river water levels with 1-day lead time. To develop these models, first the original level time series (OLTS) is decomposed into discrete wavelet components (DWCs) by discrete wavelet transform (DWT) upto three resolution levels. In WANFIS-SD, all wavelet components are used as inputs while WANFIS-MS ignores the noise wavelet components and utilizes only the effective wavelet components. The effectiveness of the developed models are evaluated through application to two Indian rivers, Kamla and Kosi, which vary significantly in their catchment area and flow patterns. The proposed models are found to forecast river water levels accurately. On comparison, the WANFIS-SD is found to perform better than WANFIS-MS for high flood levels. © 2014 Springer Science+Business Media Dordrecht
Analysis of Persistence in the Flood Timing and the Role of Catchment Wetness on Flood Generation in a Large River Basin in India
No full textThis study contributes to the understanding of the timing of occurrence of floods and role of the catchment wetness in flood processes (i.e., magnitude and the timing of floods) over one of the largest tropical pluvial river basin system, Mahanadi, in India. Being located in the monsoon ‘core’ region (18° - 28° N latitude and 73° - 82° E longitude) and its proximity to Bay of Bengal, Mahanadi River Basin (MRB) system is vulnerable to tropical depression-induced severe storms and extreme precipitation-induced fluvial floods during southwest monsoon. Here we examine the incidence of flooding over MRB in recent decades (2007-2016) using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) events at 12 stream gauges, spatially distributed over the basin. We find the mean dates of flood occurrences are temporally clustered in the month of August for all gauges irrespective of the type of flood series. Our results reveal, sensitiveness of runoff responses (Flood Magnitude, FM and the Flood Timing, FT) to lagged d-day mean catchment wetness [CW] and corresponding catchment properties. Although we identify moderate to strong positive correlation between CW and flood properties at various lags, for the MMPD events, the nature of association between CW and FM, ranges between negative to modestly positive for the catchments with fine-textured soil, whereas catchments with medium textured soil showed moderately positive correlations. Further, we find FT is more strongly correlated (as manifested by statistically significant correlations) to CW rather than FM. Overall, we observe, the correlation of CW versus FT is negative, where the flood timing is relatively irregular. The outcomes of the study helps to improve predictability of floods, which can in turn enhance existing flood warning techniques
