dc.contributor.author | Krhoda, G O | |
dc.contributor.author | Amimo, M O | |
dc.date.accessioned | 2020-01-24T12:10:38Z | |
dc.date.available | 2020-01-24T12:10:38Z | |
dc.date.issued | 2019-02 | |
dc.identifier.citation | Krhoda, G O & Amimo, M O Groundwater quality prediction using logistic regression model for garissa county groundwater quality prediction using logistic regression model for Garissa County. 𝘈𝘧𝘳𝘪𝘤𝘢 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘗𝘩𝘺𝘴𝘪𝘤𝘢𝘭 𝘚𝘤𝘪𝘦𝘯𝘤𝘦𝘴 3(0) 𝘐𝘚𝘚𝘕: 2313-3317 | en_US |
dc.identifier.issn | 2313-3317 | |
dc.identifier.uri | http://journals.uonbi.ac.ke/index.php/ajps/article/view/1797/1420 | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/107797 | |
dc.description.abstract | Groundwater quality modeling can reduce the cost of exploration and
siting of boreholes considerably. The present study applies Logistic
Regression Model to predict the probability of siting boreholes of fresh
or saline water based on geospatial data such as altitude (m),
longitudes, latitudes and depths (m), and geophysical data such as
electrical resistivity from 45 exploration sites. The geology of the study
area is represented by permeable water-bearing Tertiary-Quaternary
sediments located within the Anza Rift. The water bearing zones, or
water struck levels, range in depth between 50 and 150 m and the
average yield of about 1 - 5 m3 per hour, in the case of old wells done
using percussion rigs in the period between 1960s to the 1990s.
Recently, the discharge in the wells done using modern mud rotary
equipment yields up to 30 m3 per hour, with depths ranging between
200 to 250m below ground level. The modeling results show strong
correlation between the dependent variables; depth, mean resistivity,
longitudes, and latitudes on one hand, and salinity status of aquifers. It
is, therefore, possible to know the water quality of a location in the
study area before actual drilling is undertaken. Of all the runs made,
93% were predicted accurately while only 7% of the cases deviated
from the predicted quality. These findings prove the usefulness of the
LRM in predicting and identifying sites of high groundwater
accumulation and groundwater salinity in arid region. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | University of Nairobi | en_US |
dc.subject | Groundwater, Water quality, Prediction, Logistic, regression model | en_US |
dc.title | Groundwater quality prediction using logistic regression model for Garissa County | en_US |
dc.type | Article | en_US |