Raphael M. Wambua, Benedict M. Mutua and James M. Raude. Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya.
. 2016; 4(2):35-43. doi: 10.12691/AJWR-4-2-2
Prediction, hydro-meteorological data, ANN, data delay, auto-correlation, upper Tana River basin
This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
[1] | Dreps, C., James, A. L., Sun G and Boggs, J. (2014). Water balance of the two piedmont headwater catchments; implications for regional hydrologic landscape classification, J. of American water resources association, 50(4): 1063-1079. |
|
[2] | Botai, C. M., Botai, J. O., Muchuru, S. and Ngwana, I. (2015). Hydro-meteorological research in South Africa, A review, Water journal, 7: 1580-1594. |
|
[3] | Ismail, M. I. S, Okamoto, Y. and Okada, A. (2013). Neural network modeling for prediction of weld bead geometry in laser microwelding , Advances in optical technologies, 2013: 1-7. |
|
[4] | Lee, H. and Kang, K. (2015). Interpolation of missing precipitation data using Kernel estimations for hydrologic modeling, Advances in Meteorology, 2015: 1-12. |
|
[5] | Jang, D., Park, H. and Choi, J. T. (2015a). Create a missing precipitation data base on spatial interpolation methods not covered by a region climate change scenario, Advanced Science and Technology L, 99: 109-112. |
|
[6] | Jang, D., Park, H. and Choi, J. T. (2015b). Applicability of the kriging method in data missing area using region climate change, E-proceedings of the 36th IAHR world congress, 28th June -3rd July, 2015, the Hague, the Netherlands. |
|
[7] | Getahun, Y. S. and Gebre, S. L. (2015). Flood hazard assessment and mapping of flood inundation area of the Awash River Basin in Ethiopia using GIS and HEC-GEORAS/HEC-RAS Model, J. of Civil and Environmental Engineering, 5(4): 1-12. |
|
[8] | Rivero, C. R., Pucheta, J.,Laboret, S., Partino, D. and Sauchelli, V. (2015). Forecasting short time series with missing data by means of energy associated to series, Applied mathematics, 2015(6): 1611-1619. |
|
[9] | Belayneh, A and Adamowski, J. (2013). Drought forecasting using new machine learning methods, Journal of Water and Land development, 18(9): 3-12. |
|
[10] | Ghumman, A. R., Ghazaw, Y. M., Sohail, A. R. and Watanabe, K. (2011). Runoff forecasting by artificial neural networks and conventional model, Alexandria Engineering Journal, 50(4): 345-350. |
|
[11] | Mustafa, M. R., Isa, M. H., Rezaur, R. B. (2012). Artificial neural networks modeling in water resources engineering; infrastructure, and applications, Int. J. of Civil, Environmental, Structural, Construction and Architecture Engineering, 6(2) 128-136. |
|
[12] | Elsafi, S. H. (2014). Artificial neural networks (ANNs) for flood forecasting at Dongola station in the River Nile Sudan, Alexandria Engineering Journals, 53(3): 655-662. |
|
[13] | Valipour M. K (2014). Analysis of potential evapotranspiration using limited weather data, Appl Water Sci.. |
|
[14] | Valipour, M. (2015). Calibration of mass transfer-based models to predict reference crop evapotranspiration, Appl Water Sci.. |
|
[15] | Khoshravesh, M., Sefidkouhi M. A. G. and Valipour M. (2015). Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression and robust regression models in three arid environments, Appl Water Sci.. |
|
[16] | Maier, A. R., Jain, A., Dandy, G. C. and Sudheer, K. P. (2010). Methods used for development of neural networks for the prediction of water resource variables in river systems: current status and future directions, Journal of Environmental modeling, 25(8): 891-909. |
|
[17] | Luk K. C., Ball, J. C. and Sharma, A. (2000). A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, Journal of Hydrology, 227: 56-65. |
|
[18] | Morid, S. Smakhtin, V. and Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices International Journal of climatology, 27 (15): 2103-2111. |
|
[19] | ran, H. D., Muttil, N. and Perera, B. J C. (2009). Investigation of artificial neural network models for stream flow forecasting, 19th international congress on modeling and simulation, Perth, Australia, 12th-16th December 2011. |
|
[20] | Zali, M. A,, Retnam, A., Juahir, H. and Zain, S. M. (2011). Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia, World Applied Sciences Journal, 14: 60-65. |
|
[21] | Barua, S. (2010). Drought assessment and forecasting using a non-linear aggregated drought index, PhD thesis, Victoria University, Australia. |
|
[22] | Mishra, A. K. and Desai, V. R. (2006). Drought forecasting using feed-forward recursive models, Journal of Ecological Modeling, 198: 127-138. |
|
[23] | Campolo, M., Andreussi, P., Soldati, A. (1999). River flow forecasting with a neural network model, Water resources research, 35(34): 1191-1198. |
|
[24] | Anmala, J., Zhang, B., Govindaraju, R. S. (2000). Comparison of neural networks and empirical approaches for predicting water shed runoff, Journal of water resources, planning and management, 126(3): 156-166. |
|
[25] | Bodri, L., Cermak, V. (2000). Prediction of extreme precipitation using neural networks, application to summer and flood occurrence in Maravia, Advances in engineering software, 31: 311-321. |
|
[26] | Grimes, D. I. F., Coppola, E., Verdecchia, M., Visconti, G. (2003). A neural network approach to real time rainfall estimation for Africa using satellite data, Journal of Hydrometeorology, 4: 1119-1133. |
|
[27] | Lliunga, M., Stephenson, D. (2005). Infilling stream flow data using feed forward back-propagation (BP) artificial neural networks, applications of standard BP and pseudo Mac Laurin power series BP techniques, Water journal, 31(2): 171-176. |
|
[28] | De Silva, R. P., Daya-wansa, N. D. K., Ratnasiri, M. D. (2007). A comparison of methods used in estimating missing rainfall data, Journal of agricultural sciences, 3(2): 101-108. |
|
[29] | Sacchi, R., Ozturk, M. C., Principe, J. C.,Carneiro, A. A. F. M., Silva, I. N. D. (2007). Water inflow forecasting using the echo state network, a Brazilian case study, IEEN proceedings of international joint conference on neural network, 12-17th August 2007, Orlando FL, USA. |
|
[30] | Starrett, S. K., Heir, T., Su, Y. (2010). Filling in missing peak flow data using artificial neural networks, Journal of Engineering and Applied Sciences, 5(1) 49-55. |
|
[31] | Valipour, M. (2016). Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms, Meteorological Applications Journal, 23(2016): 91-100. |
|
[32] | NEMA. (2004). Kenya state of environment report: Chapter 7, fresh water, coastal and marine resources, Nairobi, Government printer. |
|
[33] | World Resources Institute (WRI). (2011). Kenya GIS data –world resources institute, www.wri.org/resources/data-sets/kenya-gis-data. |
|
[34] | GoK. (2012). Tana River Delta strategic environmental assessment scoping report. |
|