Clustering of Groundwater Wells and Spatial Variation of Groundwater Recharge in Sina Basin, India

A spatial and temporal analysis of groundwater levels, topography, and precipitation is required to properly manage the groundwater resource. The present paper explains it in two parts: (1) spatial analysis of groundwater levels and selection of suitable clustering approach for selection of representative wells and (2) spatial and temporal variations of groundwater recharge calculated by three numerical models: Chaturvedi model, Amritsar model and ERAS model. Four clustering techniques including K-Means clustering algorithm, Hierarchical clustering technique, canopy and expectation maximisation (EM) were used for the clustering of groundwater levels. Among these, the canopy technique presents more reliable results compared to the other techniques for the spatial analysis of groundwater levels and the formation of representative wells in the Sina basin. For the groundwater recharge estimation, Chaturvedi model and ERAS model values were found very close. The recharge values show consistency with the precipitation data and found that 15% of precipitation contributes to annual groundwater recharge. Spatio-temporal variation of groundwater recharge correlated with precipitation is also carried out for the selected basin. The results show a drastic decline in the groundwater recharge from the year 1990 to 2008. An empirical expression is also developed for groundwater recharge estimation in terms of groundwater level. This provides regional scale information about the basin and helps to understand the groundwater exploitation scenario for instance.

Prediction of excess molar volumes of binary mixtures by Prigogine-Flory-Patterson (PFP) and extended real association solution (ERAS) models

In this paper, the Prigogine?Flory?Patterson (PFP) theoretical model and the extended real association solution (ERAS) model for the prediction of the excess molar volumes of 17 binary sets of alcohols (methanol, ethanol, propan-1-ol, butan-1-ol, butan-2-ol and pentan-1-ol) with benzene, chlorobenzene, acetonitrile, hexan-1-amine and n-heptane were tested at 298.15 K and atmospheric pressure. The binary sets contained 333 experimental data points for 11 different compounds. The ERAS model demonstrated better correlative characteristics for binary mixtures comparing to PFP model. The ERAS model is successfully applied on all binary systems except ethanol + benzene and propan-1-ol + benzene. PFP model gave less satisfactory results for all systems except butan-1-ol + hexan-1-amine, for which obtained results were acceptable.