Using System Dynamic Model and Neural Network Model to Analyse Water Scarcity in Sudan

International Conference on Sustainable Energy Engineering

能源学;经济学

Li, Y.^1 ; Tang, C.^1 ; Xu, L.^2 ; Ye, S.^3

Department of Finance, Jinan University, Guangzhou

51000, China^1

Department of Statistics, Jinan University, Guangzhou

51000, China^2

Department of Mathematics, Jinan University, Guangzhou

51000, China^3

关键词: Distribution of water;    Grey prediction;    Logistic models;    Modified model;    Neural network model;    System dynamic models;    Water scarcity;    Water supply and demands;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/73/1/012013/pdf
DOI  :  10.1088/1755-1315/73/1/012013

来源: IOP

【 摘 要 】

Many parts of the world are facing the problem of Water Scarcity. Analysing Water Scarcity quantitatively is an important step to solve the problem. Water scarcity in a region is gauged by WSI (water scarcity index), which incorporate water supply and water demand. To get the WSI, Neural Network Model and SDM (System Dynamic Model) that depict how environmental and social factors affect water supply and demand are developed to depict how environmental and social factors affect water supply and demand. The uneven distribution of water resource and water demand across a region leads to an uneven distribution of WSI within this region. To predict WSI for the future, logistic model, Grey Prediction, and statistics are applied in predicting variables. Sudan suffers from severe water scarcity problem with WSI of 1 in 2014, water resource unevenly distributed. According to the result of modified model, after the intervention, Sudan's water situation will become better.