Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@PhdThesis{vu35054,

• author = "Sajal Kumar Adhikary",
• title = "Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting",
• school = "College of Engineering and Science, Victoria University",
• year = "2017",
• address = "Melbourne, Australia",
• month = "20 " # mar,
• note = "This thesis includes 1 published article for which access is restricted due to copyright (Chapters 3, 4 (first paper). Details of access to these papers have been inserted in the thesis, replacing the articles themselves.",
• keywords = "genetic algorithms, genetic programming, rivers, water basins, streams, stream-flow simulation, modeling, water supply, spatial interpolation, genetic programming-based ordinary kriging, thesis by publication",
• URL = "https://vuir.vu.edu.au/35054/",
• URL = "https://vuir.vu.edu.au/35054/7/ADHIKARY%20Sajal-thesis_Redacted.pdf",
• size = "225 pages",
• abstract = "Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians.

  The study had three components. First, an evaluation of existing Kriging methods and universal function approximation techniques such as genetic programming (GP) and ANN were performed in terms of their potentials and suitability for the enhanced spatial estimation of rainfall. The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall and the ordinary cokriging using elevation can enhance the spatial estimation of rainfall.

  Second, the design of an optimal rain gauge network was undertaken for the case study catchment using the kriging-based geostatistical approach based on the variance reduction framework. It is likely that an existing rain gauge network may consist of redundant stations, which have no contribution to the network performance for providing quality rainfall estimates. Therefore, the optimal network was achieved through optimal placement of additional stations (network augmentation) as well as eliminating or optimally relocating of redundant stations (network rationalization). In order to take the rainfall variability caused by climatic factors like El Nino Southern Oscillation into account, the network was designed using rainfall records for both El Nino and La Nina periods. The rain gauge network that gives the improved estimates of areal average and point rainfalls for both the El Nino and La Nina periods was selected as the optimal network. It was found that the optimal network outperformed the existing one in estimating the spatiotemporal estimates of areal average and point rainfalls. Additionally, optimal positioning of redundant stations was found to be highly effective to achieve the optimal rain gauge network.

  Third, an ANN-based enhanced streamflow forecasting approach was demonstrated, which incorporated the optimal rain gauge network-based input instead of using input from an existing non-optimal network to achieve the enhanced streamflow forecasting. The approach was found to be highly effective in improving the accuracy of stream-flow forecasting, particularly when the current operational rain gauge network is not an optimal one. For example, it was found that use of the optimal rain gauge network-based input results in the improvement of streamflow forecasting accuracy by 7.1percent in terms of normalised root mean square error (NRMSE) compared to the current rain gauge network based-input. Further improvement in streamflow forecasting was achieved through augmentation of the optimal network by incorporating additional fictitious rain gauge stations. The fictitious stations were added in sub-catchments that were delineated based on the digital elevation model. It was evident from the results that 18.3percent improvement in streamflow forecasting accuracy was achieved in terms of NRMSE using the augmented optimal rain gauge network-based input compared to the current rain gauge network-based input. The ANN-based input selection technique that was employed in this study for streamflow forecasting offers a viable technique for significant input variables selection as this technique is capable of learning problems involving very non-linear and complex data.",

• notes = "pii 'The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall'

  Supervisor: Nitin Muttil",

}

Genetic Programming entries for Sajal Kumar Adhikary

Citations