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

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.

[1]  Jianzhong Zhou,et al.  Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China , 2016 .

[2]  Sharad K. Jain,et al.  Modeling river stage–discharge–sediment rating relation using support vector regression , 2012 .

[3]  Mei Liu,et al.  Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river? , 2014, Environmental Science and Pollution Research.

[4]  Kwok-Wing Chau,et al.  ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS , 2015, Eng. Appl. Artif. Intell..

[5]  Hung Soo Kim,et al.  Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling , 2008 .

[6]  Armando Brath,et al.  Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling , 2007 .

[7]  Balaji Rajagopalan,et al.  Modified K-NN Model for Stochastic Streamflow Simulation , 2006 .

[8]  Olli Varis,et al.  Integrated Water Resources Management along the Senegal River: Introducing an Analytical Framework , 2002 .

[9]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[10]  M. Ghorbani,et al.  A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction , 2016, Environmental Earth Sciences.

[11]  A. Dezetter,et al.  Hydrological Evaluation of TRMM Rainfall over the Upper Senegal River Basin , 2016 .

[12]  H. Pomares,et al.  A heuristic method for parameter selection in LS-SVM: Application to time series prediction , 2011 .

[13]  M. J. Kiani,et al.  Analytical investigations of gas-sensor using methane decomposition system , 2016, Environmental Earth Sciences.

[14]  Paresh Chandra Deka,et al.  Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression , 2015 .

[15]  X. Wen,et al.  A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .

[16]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[17]  Ö. Kisi.  Generalized regression neural networks for evapotranspiration modelling , 2006 .

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Ozgur Kisi,et al.  A wavelet-support vector machine conjunction model for monthly streamflow forecasting , 2011 .

[20]  O. Ndiaye.  The Predictability of the Sahelian Climate: Seasonal Sahel Rainfall and Onset over Senegal , 2010 .

[21]  Zaher Mundher Yaseen,et al.  Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review , 2017, Theoretical and Applied Climatology.

[22]  Bijaya K. Panigrahi,et al.  Streamflow forecasting by SVM with quantum behaved particle swarm optimization , 2013, Neurocomputing.

[23]  Subimal Ghosh,et al.  Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection. , 2010 .

[24]  Gustavo Barbosa Lima da Silva,et al.  Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models , 2014 .

[25]  Zhibin He,et al.  Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions , 2015, Water Resources Management.

[26]  Honey Badrzadeh,et al.  Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting , 2013 .

[27]  B. Eswara Reddy,et al.  A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data , 2014, Appl. Soft Comput..

[28]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[29]  Marco Ghisalberti,et al.  Retention time and dispersion associated with submerged aquatic canopies , 2007 .

[30]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[31]  P. S. Kumar,et al.  Artificial Neural Network Model for Rainfall-Runoff -A Case Study , 2016 .

[32]  Chuntian Cheng,et al.  Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization , 2015 .

[33]  Ozgur Kisi,et al.  Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review , 2014 .

[34]  Zaher Mundher Yaseen,et al.  Artificial intelligence based models for stream-flow forecasting: 2000-2015 , 2015 .

[35]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[36]  Y. Xiong,et al.  Relationship between water-conservation behavior and water education in Guangzhou, China , 2015, Environmental Earth Sciences.

[37]  Thian Yew Gan,et al.  Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan , 2010 .

[38]  Mukesh K. Tiwari,et al.  Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks , 2014, Water Resources Management.

[39]  Muttucumaru Sivakumar,et al.  Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China , 2010 .

[40]  R. Deo,et al.  Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq , 2016 .

[41]  Huan Wang,et al.  A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida , 2015, Water Resources Management.

[42]  O. Kisi,et al.  Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones , 2012, Water Resources Management.

[43]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

[44]  J. G. Ndiritu,et al.  Application of radial basis function neural networks to short-term streamflow forecasting , 2010 .

[45]  Zaher Mundher Yaseen,et al.  RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia , 2015, Neural Computing and Applications.