Analysis and simulation of the influencing factors on regional water use based on information entropy

With regional socio-economic development, the gap between water use demand and available water resources in arid and humid semi-arid areas becomes increasingly serious. In this study, the size of regional water use in the Guanzhong region and Shiyang river basin in northwest China are analyzed to identify important factors affecting it, with the aim of providing better and optimal development planning for the region. Information entropy is used to measure and characterize the diversity of regional water use. Agricultural development and meteorological factors are found to be the main issues affecting regional water use in both regions. A multiple-linear regression (MLR) model was built by applying correlation coefficient (R) and mutual information (MI) scores in the process. Results show that the low value of information entropy of water use in the Shiyang river basin is due to the high proportion of agriculture water use. Using input factors chosen by MI score was found to be the best model to simulate the change of regional water use in both regions. A method using an MLR model together with MI is shown to be able to quantify the relationship between the influencing factors and water use diversity with limited available data.

[1]  Aaron D. Wyner,et al.  Prediction and Entropy of Printed English , 1993 .

[2]  Future availability of water in Egypt: the interaction of global, regional, and basin scale driving forces in the Nile Basin , 1996 .

[3]  S. Kanae,et al.  Global Hydrological Cycles and World Water Resources , 2006, Science.

[4]  V. Singh,et al.  THE USE OF ENTROPY IN HYDROLOGY AND WATER RESOURCES , 1997 .

[5]  Peter Wallensteen,et al.  Comprehensive Assessment of the Freshwater Resources of the World, International Fresh Water Resources: Conflict or Cooperation , 1997 .

[6]  Ling Tong,et al.  Temporal and spatial variations of evapotranspiration for spring wheat in the Shiyang river basin in northwest China , 2007 .

[7]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Junqiang Xia,et al.  Industrial water use kuznets curve: Evidence from industrialized countries and implications for developing countries , 2006 .

[9]  Stephen A. Billings,et al.  Radial Basis Function Network Configuration Using Mutual Information and the Orthogonal Least Squares Algorithm , 1996, Neural Networks.

[10]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[11]  M. Boyce,et al.  Evaluating resource selection functions , 2002 .

[12]  P. Döll,et al.  Development and testing of the WaterGAP 2 global model of water use and availability , 2003 .

[13]  Chong-Ho Choi,et al.  Input Feature Selection by Mutual Information Based on Parzen Window , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Maria Paula da Costa Couto.  Review of input determination techniques for neural network models based on mutual information and genetic algorithms , 2009, Neural Computing and Applications.

[15]  M. Lachniet,et al.  Use of correlation and stepwise regression to evaluate physical controls on the stable isotope values of Panamanian rain and surface waters , 2006 .

[16]  S. Kazama,et al.  Regionalization of lumped water balance model parameters based on multiple regression , 2001 .

[17]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification , 2000 .

[18]  Zhongwei Li,et al.  Multi-scale entropy analysis of Mississippi River flow , 2007 .

[19]  Li Yang,et al.  Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China , 2010, Expert Syst. Appl..

[20]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[21]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[22]  Su-Yun Huang,et al.  Model selection for support vector machines via uniform design , 2007, Comput. Stat. Data Anal..

[23]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[24]  Liesbeth Opdenacker,et al.  Development and Testing , 2021, Radial Flow Turbocompressors.

[25]  Claude E. Shannon,et al.  Prediction and Entropy of Printed English , 1951 .

[26]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[27]  Holger R. Maier,et al.  Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems , 2008, Environ. Model. Softw..

[28]  Handan Çamdevýren,et al.  Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs , 2005 .

[29]  Jing-nan Sun,et al.  Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. , 2006, Journal of environmental sciences.