GIS-based Spatio-Temporal and Geostatistical Analysis of Groundwater Parameters of Lahore Region Pakistan and their Source Characterization

2021 ◽  
Author(s):  
Sadia Ismail ◽  
M Farooq Ahmed
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Large Data ◽  
Quality Parameters ◽  
Source Characterization ◽  
Data Set ◽  
Spatio Temporal

Abstract Assessment of groundwater quality is critical, especially in the areas where it is continuously deteriorating due to unplanned industrial growth. This study utilizes GIS-based spatio-temporal and geostatistics tools to characterize the groundwater quality parameters of Lahore region. For this purpose, a large data set of the groundwater quality parameters (for a period of 2005–2016) was obtained from the deep unconfined aquifers. GIS-based water quality index (WQI) and entropy water quality index (EWQI) models were prepared using 15 water quality parameters pH (power of hydrogen), TDS (Total dissolve solids), EC (Electrical conductivity), TH (Total hardness), Ca2+ (Calcium), Mg2+ (Magnesium), Na+ (Sodium), K+ (Potassium), Cl− (Chloride), As (Arsenic), F (Fluoride), Fe (Iron), HCO3− (Bicarbonate), NO3− (Nitrate), and SO42− (Sulfate). The data analysis exhibits that 12% of the groundwater samples fell within the category of poor quality that helped to identify the permanent epicenters of deteriorating water quality index in the study area. As per the entropy theory, Fe, NO3−, K, F, SO42− and As, are the major physicochemical parameters those influence groundwater quality. The spatio-temporal analysis of the large data set revealed an extreme behavior in pH values along the Hudiara drain, and overall high arsenic concentration levels in most of the study area. The geochemical analysis shows that the groundwater chemistry is strongly influence by subsurface soil water interaction. The research highlights the significance of using GIS-based spatio-temporal and geostatistical tools to analyze the large data sets of physicochemical parameters at regional level for the detailed source characterization studies.

scholarly journals Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

2021 ◽  
Vol 11 (12) ◽  
Author(s):  
Saber Kouadri ◽  
Ahmed Elbeltagi ◽  
Abu Reza Md. Towfiqul Islam ◽  
Samir Kateb
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Groundwater Resources ◽  
Absolute Error ◽  
Quality Parameters ◽  
Support Vector ◽  
Multilinear Regression ◽  
Data Set ◽  
Artificial Neural Network Ann

AbstractGroundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

scholarly journals Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1172
Author(s):  
Purushottam Agrawal ◽  
Alok Sinha ◽  
Satish Kumar ◽  
Ankit Agarwal ◽  
Ashes Banerjee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Traditional Approach ◽  
Human Life ◽  
Large Data ◽  
Quality Parameters ◽  
Support Vector ◽  
Artificial Intelligence Techniques

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

scholarly journals Geospatial Distributions of Groundwater Quality in Gedaref State Using Geographic Information System (GIS) and Drinking Water Quality Index (DWQI)

2019 ◽  
Vol 16 (5) ◽  
pp. 731 ◽  
Author(s):  
Basheer A. Elubid ◽  
Tao Huang ◽  
Ekhlas H. Ahmed ◽  
Jianfei Zhao ◽  
Khalid. M. Elhag ◽  
...  
Keyword(s):  
Water Quality ◽  
Information System ◽  
Drinking Water ◽  
Geographic Information System ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Parameters ◽  
Geographic Information ◽  
Drinking Water Quality

The observation of groundwater quality elements is essential for understanding the classification and distribution of drinking water. Geographic Information System (GIS) and remote sensing (RS), are intensive tools for the performance and analysis of spatial datum associated with groundwater sources control. In this study, groundwater quality parameters were observed in three different aquifers including: sandstone, alluvium and basalt. These aquifers are the primary source of national drinking water and partly for agricultural activity in El Faw, El Raha (Fw-Rh), El Qalabat and El Quresha (Qa-Qu) localities in the southern part of Gedaref State in eastern Sudan. The aquifers have been overworked intensively as the main source of indigenous water supply in the study area. The interpolation methods were used to demonstrate the facies pattern and Drinking Water Quality Index (DWQI) of the groundwater in the research area. The GIS interpolation tool was used to obtain the spatial distribution of groundwater quality parameters and DWQI in the area. Forty samples were assembled and investigated for the analysis of major cations and anions. The groundwater in this research is controlled by sodium and bicarbonate ions that defined the composition of the water type to be Na HCO3. However, from the plots of piper diagram; the samples result revealed (40%) Na-Mg-HCO3 and (35%) Na-HCO3 water types. The outcome of the analysis reveals that several groundwater samples have been found to be suitable for drinking purposes in Fa-Rh and Qa-Qu areas.

GEOPHYSICAL AND WATER QUALITY INDEX SURVEYS OF GROUNDWATER QUALITY AROUND UGBOR DUMPSITE IN BENIN CITY, EDO STATE, NIGERIA, WEST AFRICA.

2016 ◽  
Vol 12 (3) ◽  
pp. 4383-4393
Author(s):  
Osabuohien Idehen
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Dry Season ◽  
Depth Range ◽  
Benin City ◽  
Physico Chemical ◽  
Vertical Electric Sounding ◽  
Geoelectric Layer

This study takes a look into groundwater quality at Ugbor Dumpsite area using water quality index (WQI), 2-Dimensional (2-D) geophysical resistivity tomography and vertical electric sounding (VES).The geophysical resistivity methods employed revealed the depth to aquifer, the geoelectric layers being made up of lateritic topsoil, clayed sand and sand. Along the trasverse line in the third geoelectric layer of lateral distance of 76 m to 100 m is a very low resistivity of 0.9 to 13 m from a depth range o f about 3 to 25 m beneath the surface- indicating contamination. Water samples were collected and analyzed at the same site during the raining season and during the dry season. The value of water quality index during the raining season was 115.92 and during the dry season was 147.43. Since values at both seasons were more than 100, it implies that the water is contaminated to some extent and therefore poor for drinking purpose. The Water Quality Index was established from important analyses of biological and physico-chemical parameters with significant health importance. These values computed for dumpsite area at Ugbor were mostly contributed by the seasonal variations in the concentrations of some parameters, such as, conductivity, total dissolved solids, hardness, alkalinity, chlorides, nitrates, calcium,  phosphates, zinc, which showed significant differences (P<0.01 and P<0.05) in seasonal variation.

Evaluation of Ground Water Quality For AL Shuwayja area –Wasit Provence Using GIS Technique

2017 ◽  
Vol 1 (2) ◽  
pp. 1-11
Author(s):  
Ali Nasser Hilo
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Ground Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Poor Quality ◽  
Quality Parameters ◽  
Ground Water Quality ◽  
Water Index ◽  
The City

The low level of water in rivers in Iraq leads to poor water quality, on that basis; we need to assess Iraq's water resources for uses of irrigation and drinking water. This study present a model accounts for ground water quality by using a water quality index (WQI) for the region defined between the city of Kut and the city of Badra in Wasit province. this study relies on a system of wells set up along the path through the Badra –Kut  and around it  up to 78 wells. The study showed poor quality of ground water in the region of study and it is unsuitability for irrigation and drinking water, as well as provided a solution to the water accumulated in the Shuwayja to reduce the bad effect on groundwater by using a system of branch and collection canals  then pumping at the effluent  of Al  Shuwayja in seasons of rainy season ..Water quality index calculated depend on the basis of various physic-chemical parameters as PH, Ec , TDS, TSS, Nacl , SO4 ,Na , and  Mg. The resultant and analytical are present with use of Arch GIS program – geostastical analysis for the water index and water quality parameters

scholarly journals Assessment of ground water quality of lahar block, Bhind district in Madhya Pradesh

2015 ◽  
Vol 3 (2) ◽  
pp. 38 ◽  
Author(s):  
Shashi Kant ◽  
Y.V. Singh ◽  
Lokesh Kumar Jat ◽  
R. Meena ◽  
S.N. Singh
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Ground Water ◽  
Irrigation Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Madhya Pradesh ◽  
Irrigation Water Quality ◽  
Irrigation Water Quality Index

<p>In sustainable groundwater study, it is necessary to assess the quality of groundwater in terms of irrigation purposes. The present study attempts to assess the groundwater quality through Irrigation Water Quality Index (IWQI) in hard-rock aquifer system and sustainable water use in Lahar block, Bhind of district, Madhya Pradesh, India. The quality of ground water in major part of the study area is generally good. In order to understand the shallow groundwater quality, the water samples were collected from 40 tube wells irrigation water. The primary physical and chemical parameters like potential Hydrogen (pH), Total Dissolved Solids (TDS), calcium (Ca<sup>2+</sup>), magnesium (Mg<sup>2+</sup>), sodium (Na<sup>+</sup>), potassium (K<sup>+</sup>), bicarbonate (HCO<sub>3</sub><sup>-</sup>), carbonate (CO<sub>3</sub><sup>2-</sup>), chloride (Cl<sup>-</sup>), and nitrate (NO<sub>3</sub><sup>-</sup>) were analyzed for (irrigation water quality index ) IWQI. The secondary parameters of irrigation groundwater quality indices such as Sodium Adsorption Ratio (SAR), Sodium Soluble Percentage (SSP), Residual Sodium Carbonate (RSC), Permeability Index (PI), and Kellies Ratio (KR) were also derived from the primary parameter for irrigation water quality index (IWQI). The IWQI was classified into excellent to unfit condition of groundwater quality based on their Water Quality Index (WQI). The IWQI (82.5%+15.0%) indicate that slightly unsustainable to good quality of ground water. Due to this quality deterioration of shallow aquifer, an immediate attestation requires for sustainable development.</p>

scholarly journals Groundwater quality assessment using water quality index (WQI) under GIS framework

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Arjun Ram ◽  
S. K. Tiwari ◽  
H. K. Pandey ◽  
Abhishek Kumar Chaurasia ◽  
Supriya Singh ◽  
...  
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Uttar Pradesh ◽  
Granite Gneiss ◽  
Total Hardness ◽  
Human Consumption ◽  
Anthropogenic Contamination ◽  
Quality Of Water

AbstractGroundwater is an important source for drinking water supply in hard rock terrain of Bundelkhand massif particularly in District Mahoba, Uttar Pradesh, India. An attempt has been made in this work to understand the suitability of groundwater for human consumption. The parameters like pH, electrical conductivity, total dissolved solids, alkalinity, total hardness, calcium, magnesium, sodium, potassium, bicarbonate, sulfate, chloride, fluoride, nitrate, copper, manganese, silver, zinc, iron and nickel were analysed to estimate the groundwater quality. The water quality index (WQI) has been applied to categorize the water quality viz: excellent, good, poor, etc. which is quite useful to infer the quality of water to the people and policy makers in the concerned area. The WQI in the study area ranges from 4.75 to 115.93. The overall WQI in the study area indicates that the groundwater is safe and potable except few localized pockets in Charkhari and Jaitpur Blocks. The Hill-Piper Trilinear diagram reveals that the groundwater of the study area falls under Na+-Cl−, mixed Ca2+-Mg2+-Cl− and Ca2+-$${\text{HCO}}_{3}^{ - }$$ HCO 3 - types. The granite-gneiss contains orthoclase feldspar and biotite minerals which after weathering yields bicarbonate and chloride rich groundwater. The correlation matrix has been created and analysed to observe their significant impetus on the assessment of groundwater quality. The current study suggests that the groundwater of the area under deteriorated water quality needs treatment before consumption and also to be protected from the perils of geogenic/anthropogenic contamination.

scholarly journals EVALUATION GROUNDWATER QUALITY BY USING GIS AND WATER QUALITY INDEX TECHNIQUES FOR WELLS IN BARDARASH AREA, NORTHERN IRAQ

2020 ◽  
Vol 53 (2C) ◽  
pp. 87-104
Author(s):  
Kaiwan Fatah
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Chemical Parameters ◽  
Alternative Source ◽  
Distribution Maps ◽  
Poor Water Quality

Studying groundwater quality in arid and semi-arid regions is essential significant because it is used as a foremost alternative source for various purposes (human and animal consumption, economic, agriculture and irrigation). Geographic Information System and Water Quality Index techniques were utilized for visualizing and evaluating the variations of groundwater quality in the studied area. Total twelve wells were sampled and twelve groundwater quality (chemical) parameters; pH, Total Alkalinity, Total Hardness (TH), Total Dissolved Solid (TDS), Electrical Conductivity (Ec), Potassium (K), Nitrate (NO3), Sulfate (SO4), Chloride (Cl), Calcium (Ca), Magnesium (Mg) and Sodium (Na) were analyzed in the laboratory. Inverse Distance Weighted technique was used as a useful tool to create and anticipate spatial variation maps of the chemical parameters. Predicting or anticipating other areas not measured, identifying them and making use of them in the future without examining samples. The results of this research showed that 8.3% of the studied wells have excellent groundwater quality, and almost sampling wells about 75% found in good groundwater quality, while findings of groundwater quality of 16.7% studied wells belong to poor water quality due to standards of Water Quality Index. Moreover, spatial analysis in term of groundwater quality map showed that Excellent groundwater quality was detected in well 3, very good groundwater potential was noticed in six studied wells (wells 2, 6, 8, 10, 11 and 12), and other sampling wells (wells 4 and 7) were observed as good groundwater quality, while poor water quality was observed in wells (well 1 and 5). Hence, spatial distribution maps showed that the almost groundwater quality in the area about 1046.82 km² (99.04%) are suitable for drinking purpose, whereas proximate 10.18 km² (0.96%) are observed as poor water quality and inappropriate for consumptions especially in the southern part of the area.

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