Estimation of domestic Water demand in Fringe Area of Ajmer City (Rajasthan) India -A Case Study Singh Ganpat1, Goel Arun2, Choudhary Mahender3 1Assistant, Professor, Civil Engg., GEC, Ajmer, India 2Professor, Civil Engg., NIT, Kurukshetra, India 3Professor, Civil Engg., MNIT, Jaipur, India Online Published on 21 June, 2022. Abstract Assessment of domestic water demand is quite important to understand the behaviour of domestic water demand affecting variables with their actual water demand of further expansion of any city. The current study has proposed a multilinear regression stepwise model using principal components analysis to estimate domestic water demand for fringe area of Ajmer city (Ragasthan state). Sixteen variables comprising of socio-economic, demographic and climatic factors have been collected from the different parts of under developed area of the city Ajmer, Rajasthan state, India. Further, these variables are analysed to generate the reduction factors (PCs) by using factor reduction technique under principal components analysis. These reduction factors are used as input factors/variables in multi linear regression stepwise and developed goodness-of-fit models (M1 to M6) on the basis of statically significant. This model M6 gives the maximum value of regression coefficient R2 = 0.77. In order to examine the similarities and importance of this model, the best model M6 was compared with multilayer perceptron artificial neural network (MLP NN) model based on same four PCs reduction factors/variables whose regression coefficient was R2 = 0.79. For the model M6, it was observed that the results of MLP NN based modelling are quite close to the regression analysis based modelling considering principal component analysis. The results also represented that both the model PCR (PCs) and MLP (NN) PCs are able to assess the domestic water demand appropriately. Finally an equation using model M6 has been proposed to estimate the domestic water demand using six principal components based on sixteen variables in underdeveloped area of city, Ajmer city, Rajasthan (India). Top Keywords Factor analysis, Principal components analysis, Principal component regression (stepwise), Domestic water demand, Neural network. Top |