Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: The case of the Lower Shire floodplain, Malawi

Hydrology Research

Volumn 45, Issue 6, 2014, Pages 838-854

  Mwale, F D ^a   Adeloye, A J ^a   Rustum, R ^b  

^a HERIOT WATT UNIVERSITY   (United Kingdom)

^b HERIOT WATT UNIVERSITY   (United Arab Emirates)

Author keywords

Data poor catchments; Forecasting; Multi layer perceptron artificial neural networks; Self organising maps; Streamflow; Water level

Indexed keywords

BANKS (BODIES OF WATER); CATCHMENTS; DEVELOPING COUNTRIES; FLOOD CONTROL; FORECASTING; NEURAL NETWORKS; RISK MANAGEMENT; RUNOFF; SELF ORGANIZING MAPS; STREAM FLOW; WATER LEVELS; WEATHER FORECASTING;

COEFFICIENT OF CORRELATION; DATA-DRIVEN MODEL; FLOOD FORECASTING; FLOOD PROTECTION; FLOOD RISK MANAGEMENT; HYDROLOGICAL MODELLING; MULTI LAYER PERCEPTRON; WATER LEVEL FORECASTING;

FLOODS;

ARTIFICIAL NEURAL NETWORK; DATA QUALITY; FLOOD CONTROL; FLOOD FORECASTING; HYDROLOGICAL MODELING; PARADIGM SHIFT; STREAMFLOW; WATER LEVEL;

MALAWI;

EID: 84919914664     PISSN: 19989563     EISSN: 22247955     Source Type: Journal    
DOI: 10.2166/nh.2014.168     Document Type: Article

References (70)

1. Abrahart, R. J. & White, S. M. 2000 Modelling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets. Physics and Chemistry of the Earth (B) 26, 19-24.
2. Adeloye, A. J. & De Munari, A. 2006 Artificial neural network based generalized storage-yield-reliability models using Levenberg-Marquardt algorithm. Journal of Hydrology 362, 215-230.
3. Adeloye, A. J. & Rustum, R. 2012 Self-organising map rainfall-runoff multivariate modelling for runoff reconstruction in inadequately gauged basins. Hydrology Research 43, 603-617.
4. Alvisi, S., Mascellani, G., Franchini, M. & Bardossy, A. 2006 Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Sciences 10, 1-17.
5. Aquil, M., Kita, I., Yano, A. & Nishiyama, S. 2007 Neural networks for real time catchment flow modeling and prediction. Water Resources Management 21, 1782-1796.
6. Ayalew, L., Möller, D. P. F. & Reik, G. 2007 Using Artificial Neural Networks (ANN) for real time flood forecasting, the Omo River case in Southern Ethiopia. Proceedings of the 2007 Summer Computer Simulation Conference, Society for Computer Simulation International, San Diego, USA.
7. Baxter, C. W., Zhang, Q., Stanley, S. J., Shariff, R., Tupas, R. T. & Stark, H. L. 2000 Drinking water quality and treatment: the use of artificial neural networks. Canadian Journal of Civil Engineering 28, 26-35.
8. Bowden, G. J., Dandy, G. C. & Maier, H. R. 2005 Input determination for neural network models in water resources applications. Part 1-background and methodology. Journal of Hydrology 301, 75-92.
9. Chau, K. W., Wu, C. L. & Li, Y. S. 2005 Comparison of several forecasting models in the Yangtse River. Journal of Hydrologic Engineering 10, 485-491.
10. Cigizoglu, H. K. 2004 Estimation and forecasting of daily suspended sediment data by multi layer perceptrons. Advances in Water Resources 27, 185-195.
11. Dawson, C. W. & Wilby, R. L. 2000 Hydrological modelling using artificial neural networks. Progress in Physical Geography 25, 80-108.
12. Demuth, H., Beale, M. & Hagan, M. 2009 Neural Network Toolbox™ 6 User's Guide.
13. Diamantopoulou, M. J., Antonopoulos, V. Z. & Papamichail, D. M. 2005 The use of neural network technique for the prediction of water quality parameters of Axios River in Northern Greece. European Water 11/12, 55-62.
14. Dutta, D., Welsh,W. D., Vaze, J., Kim, S. S. H. & Nicholls, D. 2012 A comparative evaluation of short-term streamflow forecasting using time series analysis and rainfall-runoff models in ewater source. Water Resources Management 26, 4397-4415.
15. EU Directive 2007 Directive 2007/60/EC of the European parliament and of the council. Official Journal of the European Union L288, 27-34.
16. Haykin, S. 1999 Neural Networks: A Comprehensive Foundation. Prenticee Hall, New Jersey.
17. Hung, N. Q., Babel, M. S., Weesakul, S. & Tripathi, N. K. 2009 An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences 13, 1413-1425.
18. Iliadis, L. S. & Maris, F. 2007 An Artificial Neural Network model for mountainous water-resources management: the case of Cyprus mountainous watersheds. Environmental Modelling & Software 22, 1066-1072.
19. Ilunga, M. & Stephenson, D. 2005 Infilling streamflow data using feed-forward Back-Propagation (BP) artificial neural networks: application of standard BP and Pseudo Mac Laurin Power Series BP techniques. Water SA 31, 171-176.
20. Isik, S., Kalin, L., Schoonover, J. E., Srivastava, P. & Lockaby, B. G. 2013 Modeling effects of changing land use/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. Journal of Hydrology 485, 103-112.
21. Ito, F. & Murata, A. 2009 Artificial neural network model estimating land use change in the Southwestern Part of Nagareyama City, Chiba Prefecture. In: New Frontiers in Urban Analysis (Y. Asami, Y. Sadahiro & T. Ishikawa, eds). CRC Press, Boca Raton, pp. 65-79.
22. Jain, S. K. 1993 Calibration of conceptual models for rainfall-runoff simulation. Hydrological Sciences Journal 38, 431-441.
23. Jain, S. K. & Sudheer, K. P. 2008 Fitting of hydrological models: a close look at the Nash-Sutcliffe Index. Journal of Hydrologic Engineering 13, 981-986.
24. Kalteth, A. M. & Berndtsson, R. 2007 Interpolating monthly precipitation by Self Organizing Map (SOM) and Multilayer Perceptron (MLP). Hydrological Sciences 52, 305-317.
25. Khamis, A., Ismail, Z., Haron, K. & Mohammed, A. T. 2006 Neural network model for oil palm yield modelling. Journal of Applied Sciences 6, 391-399.
26. Kirpich, Z. P. 1940 Time of concentration from small agricultural watersheds. Civil Engineering 10, 362.
27. Kohonen, T., Oja, E., Simula, O., Visa, A. & Kangas, J. 1996 Engineering application of the self organizing map. IEEC Proceedings 84, 1358-1384.
28. Kundzewicz, Z. W. & Menzel, L. 2005 Natural flood reduction strategies-a challenge. International Journal of River Basin Management 3, 125-131.
29. Legates, D. R. & Mccabe, G. J. 1999 Evaluating the use of goodness-of fit measures in hydrologic and hydroclimatic model validation. Water Resources Research 35, 233-241.
30. Liu, Z., Martina, M. L. & Todini, E. 2005 Flood forecasting using a fully distributed model: application of the TOPKAPI model to the upper Xixian catchment. Hydrology and Earth System Sciences 9, 347-364.
31. Liukkonen, M., Laakso, I. & Hiltunen, Y. 2013 Advanced monitoring platform for industrial wastewater treatment: multivariate approach using the self-organizing map. Environmental Modelling and Software 48, 193-201.
32. Lumbroso, D., Ramsbottom, D. & Spaliveiro, M. 2008 Sustainable flood risk management strategies to reduce rural communities' vulnerability to flooding in Mozambique. Journal of Flood Risk Management 1, 34-42.
33. Maier, H. R. & Dandy, G. C. 2000 Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software 25, 101-124.
34. Maier, H. R., Jain, A., Dandy, G. C. & Sudheer, D. 2010 Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling and Software 891-909.
35. Mazvimavi, D. 2003 Estimation of Flow Characteristics of Ungauged Catchments: Case Study in Zimbabwe. PhD Thesis, Wageningen University.
36. MOIWD (undated). Flood Forecasting and Warning Sytem for Lower Shire Valley Project Status Report. Lilongwe Malawi.
37. Moore, R. J. & Bell, V. A. 2000 Comparison of Rainfall-Runoff Models for Flood Forecasting. Part 1: Literature Review of Models. Institute of Hydrology, Bristol
38. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. & Veith, T. 2007 Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transaction of the ASABE 50, 885-900.
39. Mwale, F. D., Adeloye, A. J. & Rustum, R. 2012 Infilling of missing rainfall and streamflow data in the Shire River basin, Malawi-a self organizing map approach. Journal of Physics and Chemistry of the Earth 50-52, 34-43.
40. National Statistical Office 2012 Integrated Household Survey 2010-2011-Household Socio-economic Characteristics Report, Zomba, Malawi.
41. Nayak, P. C., Rao, S. Y. R. & Sudheer, K. P. 2006 Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management 20, 77-90.
42. Ngongondo, C. S. X., Chong, Y. u., Tallaksen, L. M., Alemaw, B. & Chirwa, T. 2011 regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and Lmoments approaches. Stochaistic Environmental Research and Risk Assessment 25, 939-955.
43. O'Connor, K. M. 2005 River flow forecasting. In: River Basin Modelling for Flood Risk Mitigation (D. W. Knight & A. Y. Shamseldin, eds). Taylor & Francis, London.
44. Okkan, U. 2011 Application of Levenberg-Marquardt optimization algorithm based multilayer neural networks for hydrological time series modeling. An International Journal of Optimization and Control: Theories and Applications 1, 53-63.
45. Panda, S. S., Garg, V. & Chaubey, I. 2004 Artificial neural networks application in lake water quality estimation using satellite imagery. Journal of Environmental Informatics 4, 65-74.
46. Plate, E. J. 2009 Classification of hydrological models for flood management. Hydrology and Earth System Sciences 13, 1939-1951.
47. Primožic?, M., Kobold, M. & Brilly,M. 2008 The implementation of the HBV model on the Sava River Basin. In: XXIVth Conference of the Danubian Countries, Earth and Environmental Science. http://dx.doi.org/10.1088/1755-1307/4/1/012004.
48. Rahman, M., Goel, N. & Arya, D. 2012 Development of the Jamuneswari flood forecasting system: case study in Bangladesh. Journal of Hydrologic Engineering 17, 1123-1140.
49. Rustum, R. & Adeloye, A. J. 2007 Replacing outliers and missing values from activated sludge data using Kohonen selforganizing map. Journal of Environmental Engineering 133, 909-916.
50. Rustum, R. & Adeloye, A. J. 2012 Improved modelling of wastewater treatment primary clarifier using hybrid ANNs. International Journal of Computer Science and Artificial Intelligence 2, 14-22.
51. Sahoo, G. B., Ray, C. & De Carlo, E. H. 2006 Calibration and validation of a physically distributed hydrological model, MIKE SHE, to predict streamflow at high frequency in a flashy mountainous Hawaii stream. Journal of Hydrology 327, 94-109.
52. Saliha, A. H., Awulachew, S. B., Cullmann, J. & Horlacher, H. 2011 Estimation of flow in ungauged catchments by coupling a hydrological model and neural networks: case study. Hydrology Research 42, 386-400.
53. Samuels, P., Klijn, F. & Dijkman, J. 2006 An analysis of the current practice of policies on river flood risk management in different countries. Irrigation and Drainage 55, 141-150.
54. Shamseldin, A. Y. 1997 Application of a Neural Network Technique to Rainfall-Runoff Modelling. Journal of Hydrology 199, 272-294.
55. Shela, O. N., Thompson, G., Jere, P. & Annandale, G. 2008 Analysis of Lower Shire Floods & a Flood Risk Reduction and Recovery Programme Proposal for the Lower Shire Valley, Department of Disaster Management Affairs, Lilongwe, Malawi.
56. Singh, R. D. 2005 Real-Time Flood Forecasting-Indian Experiences. International G-WADI Modelling Workshop, Roorkee, India.
57. Solomantine, D. P. & Dulal, K. N. 2003 Model Trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal 48, 399-411.
58. Sudheer, K. P., Gosain, A. K. & Ramasastri, K. S. 2002 A datadriven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes 16, 1325-1330.
59. SVADD, Shire Valley Agricultural Development Project 1975 An Atlas of the Lower Shire Valley, Malawi. Department of Surveys, Blantyre.
60. Teschl, R. & Randeu,W. L. 2006 A neural network model for short term river prediction. Natural Hazards and Earth Systems Sciences 6, 629-635.
61. Thielen, J., Bartholmes, J., Ramos, M. H. & De Roo, A. 2009 the European flood alert system-part 1: concept and development. Hydrology and Earth System Sciences 13, 125-140.
62. Todini, E. 1996 The ARNO rainfall-runoff model. Journal of Hydrology 175, 339-382.
63. Toth, E. & Brath, A. 2007 Multistep ahead streamflow forecasting: role of calibration data in conceptual and neural network modeling. Water Resources Research, 43.
64. UNISDR 2005 Hyogo Framework for Action 2005-2015: Building the Resilience of Nations and Communities to Disasters. Geneva.
65. Van Ogtrop, F. F., Hoekstra, A. Y. & Van Der Meulen, F. 2005 Flood management in the Lower Incomati River Basin, Mozambique: two alternatives. Journal of the American Water Resources Association 41, 607-619.
66. WMO/GWP 2009 Integrated Flood Risk Management Concept Paper. Geneva.
67. Wu, C. L. & Chau, K. W. 2011 Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology 399, 394-409.
68. Wu, C. L., Chau, K.W. & Li, Y. S. 2009 Methods to improve neural network performance in daily flows prediction. Journal of Hydrology 372, 80-93.
69. Yawson, D. K., Kongo, V. M. & Kachroo, R. K. 2005 Application of linear and non-linear techniques in riverflow forecasting in Kilombero River Basin, Tanzania. Hydrologic Sciences 50, 783-796.
70. Yuan, H., Van Der Wiele, C. F. & Khorram, S. 2009 An automated artificial neural network system for land use/land cover classification from Landsat TM Imagery. Remote Sensing 1, 243-265.