Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins

Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupl...

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Published in:	Theoretical and applied climatology Vol. 145; no. 3-4; pp. 1207 - 1231
Main Authors:	Bajirao, Tarate Suryakant, Kumar, Pravendra, Kumar, Manish, Elbeltagi, Ahmed, Kuriqi, Alban
Format:	Journal Article
Language:	English
Published:	Vienna Springer Vienna 01.08.2021 Springer Springer Nature B.V
Subjects:	Adaptive systems Algorithms Analysis Aquatic Pollution Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate science Climatology Daily Daily rainfall Daily runoff Data Decomposition Earth and Environmental Science Earth Sciences Fuzzy logic hybrids India Inference Model accuracy Neural networks Original Paper Performance evaluation prediction Predictions Rain Rain and rainfall Rainfall Reliability analysis River basins Rivers Runoff Sensitivity analysis time series analysis Uncertainty analysis Waste Water Technology Water Management Water Pollution Control Watershed management watersheds India
ISSN:	0177-798X, 1434-4483
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Abstract	Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Q t–1 ) is the most crucial variable for daily runoff prediction.
AbstractList	Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Qₜ–₁) is the most crucial variable for daily runoff prediction. Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Qt–1) is the most crucial variable for daily runoff prediction. Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Q t–1 ) is the most crucial variable for daily runoff prediction. Accurate prediction of daily runoff's dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Q.sub.t-1) is the most crucial variable for daily runoff prediction.
Audience	Academic
Author	Kuriqi, Alban Bajirao, Tarate Suryakant Kumar, Manish Elbeltagi, Ahmed Kumar, Pravendra
Author_xml	– sequence: 1 givenname: Tarate Suryakant surname: Bajirao fullname: Bajirao, Tarate Suryakant organization: Department of Soil Science and Agriculture Chemistry, School of Agriculture, Lovely Professional University – sequence: 2 givenname: Pravendra surname: Kumar fullname: Kumar, Pravendra organization: Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology – sequence: 3 givenname: Manish surname: Kumar fullname: Kumar, Manish organization: Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology – sequence: 4 givenname: Ahmed surname: Elbeltagi fullname: Elbeltagi, Ahmed organization: Agricultural Engineering Department, Faculty of Agriculture, Mansoura University – sequence: 5 givenname: Alban orcidid: 0000-0001-7464-8377 surname: Kuriqi fullname: Kuriqi, Alban email: alban.kuriqi@tecnico.ulisboa.pt organization: CERIS, Instituto Superior Técnico, Universidade de Lisboa
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SubjectTerms	Adaptive systems Algorithms Analysis Aquatic Pollution Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate science Climatology Daily Daily rainfall Daily runoff Data Decomposition Earth and Environmental Science Earth Sciences Fuzzy logic hybrids India Inference Model accuracy Neural networks Original Paper Performance evaluation prediction Predictions Rain Rain and rainfall Rainfall Reliability analysis River basins Rivers Runoff Sensitivity analysis time series analysis Uncertainty analysis Waste Water Technology Water Management Water Pollution Control Watershed management watersheds
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Title	Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins
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