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170 results on '"Kasiviswanathan, K."'

4. Forecasting of Soil Moisture: An ANN Model Integrated with Empirical Mode Decomposition

Author

Thakur, Chandni, Aadhityaa, M., Vijayalakshmanan, S., Kasiviswanathan, K. S., Soundharajan, B., Sen, Subhamoy, Tummuru, Narsa Reddy, Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, Gawad, Iman O., Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, Ergüler, Zeynal Abiddin, editor, Hadji, Riheb, editor, Chaminé, Helder I., editor, Rodrigo-Comino, Jesús, editor, Kallel, Amjad, editor, Merkel, Broder, editor, Eshagh, Mehdi, editor, Chenchouni, Haroun, editor, Grab, Stefan, editor, Karakus, Murat, editor, Khomsi, Sami, editor, Knight, Jasper, editor, Bezzeghoud, Mourad, editor, Barbieri, Maurizio, editor, Panda, Sandeep, editor, Benim, Ali Cemal, editor, and El-Askary, Hesham, editor

Published
2023
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18. A Bayesian modelling approach for assessing non-stationarity in annual maximum rainfall under a changing climate

Author

Zelalem, Temesgen and Kasiviswanathan, K. S.

Abstract

Potential changes in hydro-meteorological events have been causing mass damage to the economy and lives. Among several factors, the progression of climate change over a long time is expected to cause the non-stationarity in annual maximum rainfall. Understanding the characteristics of annual maximum rainfall series is crucial for coastal cities as they are highly vulnerable due to the largely varying weather patterns. In this paper, we propose stationary and non-stationary methods to model the effect of non-stationarity on the different duration of annual maximum rainfall and demonstrate the impacts on nine coastal cities spread across the Arabian and Bay of Bengal sea stretch of India. The Bayesian Inference parameter estimation technique was used. It was found that while stationary models have been often found to fit well for longer-duration rainfall, non-stationary models have been often observed to best fit the short duration.

Published
2023
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40. Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models.

Author

Kasiviswanathan, K. and Sudheer, K.

Subjects
*ARTIFICIAL neural networks, *ARTIFICIAL intelligence, *HYDROLOGIC cycle, *HYDROLOGICAL research, *STREAMFLOW
Abstract

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement. [ABSTRACT FROM AUTHOR]

Published
2017
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