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Severe drinking water shortage affects all resident of the Kabul river basin. Two and a half decades of civil war in Afghanistan (it began in late 1978) have resulted in widespread environmental degradation and water resource development throughout the country. The war has already finished and, therefore, water resource management for supplying water is one of the most important tasks for Afghanistan's government. The Kabul river basin which is the most populated area in the country is located in the eastern part of Afghanistan. This article deals with the water resource properties of the Kabul river basin and also water demand in the important cities of the basin, such as Kabul, the capital and the largest city in the country. Also a few suggestions for providing water for domestic and agriculture purposes in short term, medium time and long term have been discussed.

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Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn't available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as "dynamic regression" compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave velocity in wells without this parameter. H. Akhundi et al.304

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