The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River

Diop, Lamine; Bodian, Ansoumana; Djaman, Koffi; Yaseen, Zaher Mundher; Deo, Ravinesh C.; El-shafie, Ahmed; Brown, Larry C.

doi:10.1007/s12665-018-7376-8

Original Article
Published: 27 February 2018

The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River

Lamine Diop^1,2,
Ansoumana Bodian³,
Koffi Djaman⁴,
Zaher Mundher Yaseen ORCID: orcid.org/0000-0003-3647-7137⁵,
Ravinesh C. Deo⁶,
Ahmed El-shafie⁷ &
Larry C. Brown²

Environmental Earth Sciences volume 77, Article number: 182 (2018) Cite this article

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36 Citations
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Abstract

Ideal prediction and modeling of stream-flow and its hydrological applications are extremely significant for decision-making tasks and proper planning of water resource and hydraulic engineering. In the last two decades, the potential of soft computing approaches has increased dramatically in engineering and science problems. In this research, the utility of two soft computing approaches, namely support vector regression (SVR) model and generalized regression neural network (GRNN), is validated to predict 1 day ahead daily river flow data in the upper Senegal River basin at the Bafing Makana station in West Africa. The modeling is conducted by including the climatological information in the modeled stream-flow patterns. Correlation procedure is established and applied to obtain the modeling of the input variables with statistically significant lagged datasets at t − 1, t − 2, and t − 3 used as three input combination for each case study scenario. Different statistical indicators are used to evaluate the accuracy of the prediction models. The results show that the accuracy of the models varied by the scenario and the input datasets, where the SVR model yielded the best results for both modeling scenarios. It is also evident that combining the historical stream-flow data with the rainfall and evapotranspiration can ameliorate substantially the accuracy of the two models for predicting 1-day ahead stream-flow. A comparison of the optimal SVR and GRNN models in this problem indicates that SVR exhibits superior performance to the GRNN model in estimating the daily stream-flow data, irrespective of the modeling scenario and the datasets that is applied. The findings offer an opportunity to apply SVR model for predicting daily stream-flow, with less data requirement for the investigated Senegal River basin.

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Authors and Affiliations

UFR S2ATA « Sciences Agronomiques, de l’Aquaculture et des Technologies Agroalimentaires », Université Gaston Berger (UGB), BP 234, Saint Louis, Senegal
Lamine Diop
Department of Food, Agricultural and Biological Engineering, The Ohio State University, 590 Woody Hayes Dr., Columbus, OH, 43210, USA
Lamine Diop & Larry C. Brown
Laboratoire Leïdi « Dynamique des territoires et développement », Université Gaston Berger (UGB), BP 234, Saint Louis, Senegal
Ansoumana Bodian
Department of Plant and Environmental Sciences, Agricultural Science Center at Farmington, New Mexico State University, P.O. Box 1018, Farmington, NM, 87499, USA
Koffi Djaman
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Zaher Mundher Yaseen
School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences (ICACS), Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, Toowoomba, Australia
Ravinesh C. Deo
Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
Ahmed El-shafie

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Lamine Diop
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Ansoumana Bodian
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Koffi Djaman
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Zaher Mundher Yaseen
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Ravinesh C. Deo
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Ahmed El-shafie
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Larry C. Brown
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Correspondence to Zaher Mundher Yaseen.

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Diop, L., Bodian, A., Djaman, K. et al. The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River. Environ Earth Sci 77, 182 (2018). https://doi.org/10.1007/s12665-018-7376-8

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Received: 15 November 2017
Accepted: 20 February 2018
Published: 27 February 2018
DOI: https://doi.org/10.1007/s12665-018-7376-8

Keywords

Climatic inputs
Stream-flow pattern
Forecasting
SVR
GRNN
Senegal River basin