Risk analysis of rich–poor rainfall encounter in inter-basin water transfer projects based on Bayesian networks

An inter-basin water transfer project is one of the effective ways to resolve the problem of an uneven distribution of water resources. Temporal and spatial variations in rainfall in different basins greatly affect water supply and demand in inter-basin water transfer projects, leading to risks to the operation of the water transfer projects. This paper applies a Bayesian network model to analyze this risk and studies the rich–poor rainfall encounter risk between a water source area and water receiving areas in the middle route of the South-to-North Water Transfer Project in China. Real time scenario simulations with the input of new observations were also studied. The results show that the rich–poor rainfall encounter risk is high for the Tangbai River receiving area in the fourth quarter, for the Huai River and South of Hai River receiving area in the second quarter, and for the North of the Hai River receiving area in the fourth and first quarters. The scenario simulations reflect risk change in the operation of water transfer projects, providing scientific decision support for the management of the water resource distribution in the inter-basin water transfer projects.

[1]  Guido Sonnemann,et al.  Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator , 2003 .

[2]  Rashid M. Hassan,et al.  An ecological economics framework for assessing environmental flows: the case of inter-basin water transfers in Lesotho , 2005 .

[3]  Ximing Cai.  Water stress, water transfer and social equity in Northern China--implications for policy reforms. , 2008, Journal of environmental management.

[4]  C. Ma,et al.  A study on the environmental geology of the Middle Route Project of the South–North water transfer , 1999 .

[5]  Wim G.M. Bastiaanssen,et al.  Irrigation water distribution and long-term effects on crop and environment , 2001 .

[6]  Füsun Ülengin,et al.  A Bayesian causal map for inflation analysis: The case of Turkey , 2006, Eur. J. Oper. Res..

[7]  Joyeeta Gupta,et al.  Interbasin water transfers and integrated water resources management: Where engineering, science and politics interlock , 2008 .

[8]  J. Zhao,et al.  Influence of the South-North Water Diversion Project and the mitigation projects on the water quality of Han River. , 2008, The Science of the total environment.

[9]  Hongxing Zheng,et al.  South-to-north Water Transfer Schemes for China , 2002 .

[10]  Ken-ichi Funahashi,et al.  Multilayer neural networks and Bayes decision theory , 1998, Neural Networks.

[11]  Andrea Castelletti,et al.  Bayesian Networks and participatory modelling in water resource management , 2007, Environ. Model. Softw..

[12]  Jouko Lampinen,et al.  Bayesian approach for neural networks--review and case studies , 2001, Neural Networks.

[13]  Vladimir U. Smakhtin,et al.  Modeling water supply and demand scenarios: the Godavari–Krishna inter-basin transfer, India , 2009 .

[14]  Pierre Baldi,et al.  On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks , 2005, Neural Networks.

[15]  Paolo Trucco,et al.  A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation , 2008, Reliab. Eng. Syst. Saf..

[16]  Rashid M. Hassan,et al.  Integrated ecological economics accounting approach to evaluation of inter-basin water transfers: An application to the Lesotho Highlands Water Project , 2006 .

[17]  Thomas A. Runkler,et al.  Using a Local Discovery Ant Algorithm for Bayesian Network Structure Learning , 2009, IEEE Transactions on Evolutionary Computation.

[18]  U. C. Chaube,et al.  90. Analysis of a Large Inter-basin Water Transfer System in India , 2005 .