In this study, we investigated the application of Geographic Information Systems (GIS) for groundwater quality assessment through the integration of statistical models with soil and topographical data. Our primary objectives were to identify soil parameters and topographical attributes contributing to groundwater quality assessment and to evaluate the potential of geostatistics and GIS for spatial analysis of groundwater resources. Groundwater samples were collected from 43 agricultural wells, and surface soil layer samples (0–20 cm) were obtained near each well. We measured groundwater quality parameters and relevant soil properties. Our approach involved the utilization of multiple linear regression (MLR) and principal component regression (PCR), combined with topographical terrain attributes and soil data, for modeling groundwater electrical conductivity (GEC). Our findings revealed significant correlations between GEC and soil electrical conductivity (EC) (r = 0.89) as well as soil carbonate (CaCO3) (r = 0.68). Among the ten topographical attributes considered, the terrain wetness index (TWI) exerted the highest influence on GEC (r = 0.57), followed by the slope (r = -0.47). Further analysis demonstrated that the MLR model outperformed the PCR model in both the development and calibration datasets, with an achieved R 2 value of 0.89 and a root mean square error (RMSE)of 150 μScm -1 for MLR, compared to an R 2 of 0.85 and an RMSE of 170 μScm -1 for PCR when coupled with soil and attribute data for GEC prediction. The resulting GEC map generated from the MLR model displayed spatial variations, ranging from 605 μScm -1 in the northern region to 1275 μScm -1 in the central part of the study site. In conclusion, our study demonstrated the effectiveness of combining statistical modeling with geostatistics and GIS for groundwater quality assessment, providing valuable insights for resource management and environmental planning.
Citation: Chen Y (2023) GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data. PLoS ONE 18(11): e0292680. https://doi.org/10.1371/journal.pone.0292680
Editor: AL MAHFOODH, UNITEN: Universiti Tenaga Nasional, MALAYSIA
Received: June 27, 2023; Accepted: September 26, 2023; Published: November 30, 2023
Copyright: © 2023 Yuwen Chen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Water supply for arid-semi-arid regions like Iran depends heavily on groundwater resources. In addition to that, there are cities and towns without wastewater treatment establishments. Owing to effects such as precipitation, the permeability, and agriculture [1, 2] makes it impossible for wastewater to be utilized and recycled for other purposes [3]. Several provinces in Iran’s south, which have semi-arid and arid climates, rely on groundwater as their primary source of fresh water for irrigation [4, 5]. In recent decades due to population growth, the amount of groundwater used by humans has increased in a considerable manner. This includes drinking, agriculture, industry, and many other purposes, making wastewater treatment challenging. These impacts on the downstream environment and groundwater are complicated [6]. To better manage groundwater reservoirs, it is imperative to analyze groundwater quality in addition to monitoring the groundwater levels [3–7]. To accurately measure groundwater quality for various components related to groundwater, conventional methods are commonly used to obtain an indication of a particular parameter as well as its complexity. Generally, these methods involve a considerable amount of time, money, and labor and require a lot of expertise. Some regions also present difficulties in sampling groundwater. The lithology, morphology, soil formation related to groundwater health, slope, dam construction and topography of the land are generally associated with groundwater quality [8–10]. Therefore, new big-data analyses are required to analyze groundwater, since traditionally, environmental data is interpreted using statistical models [3, 7, 11].
Because the pH of groundwater is relatively constant in Iran aquifers because carbonate formations are abundant in the majority of the country, particularly in the southern lands, it cannot function as a reliable indicator of groundwater quality [5, 12]. However, some research groups have tried to use this indicator as a measurement of potential groundwater quality. For example, Osman et al. [13] found declining groundwater levels in Malaysia. They developed a predictive statistical model based on 11 months of data. This model outperformed others, especially when considering 1-day delayed groundwater levels as input (R 2 = 0.92). Their study sets a strong benchmark for groundwater quality predictions in the future. Irwan et al. [14] emphasized the critical role of water in agriculture and daily life, highlighting its impact on various aspects. They reviewed 83 studies from 2009 to 2023, focusing on water quality prediction methods and artificial intelligence models. They discussed the potential of generative adversarial networks (GANs) and transformers to improve water quality prediction by addressing data limitations. In areas with a wide range of electrical conductivity (EC), groundwater EC is a better index of groundwater quality. The development of statistical models is possible since simple assessments of groundwater quality cannot suffice for various purposes, particularly at large scales where lots of sampling wells are present. Besides the common linear regression method, principal component (PC) regression is also a reliable approach for examining groundwater quality [6–8]. The MLR method is used to develop forecasting models based on the results of the PCR to find relationships between quality parameters. Moreover, MLR is useful for determining whether one or more variables are more important, as well as for identifying outliers and anomalies. Because GIS provides readily suitable methods for manipulating spatial data, groundwater quality can now be evaluated quickly and efficiently using GIS as a visual tool [3, 4]. Statistical modeling and groundwater quality are integrated to successfully map, manage and protect groundwater.
Combining statistical modeling, geostatistics, and GIS for groundwater quality assessment offers numerous advantages [1, 15]. It enables the visualization of spatial trends, the integration of diverse data types, and the generation of accurate groundwater quality maps. This integrated approach also supports data validation, decision-making, and risk assessment. Furthermore, it facilitates the comprehension of temporal fluctuations and trends in groundwater quality. This approach has a wide range of potential, including environmental management, water resource planning, contamination detection, public health evaluation, and more. It aids in informed decision-making and resource management across various sectors. However, accurate results of this combination rely on data quality and availability, demanding high-resolution data and expertise. Model complexity affects predictability, and validation can be challenging with limited data. It may not fully address short-term changes or causality but remains a valuable tool for groundwater quality management when used judiciously.
The GIS and statistical methods have been combined in several studies to analyze spatial groundwater quality [16–18]. A GIS technic integrated with a statistical approach was documented by Haghizadeh et al. [3] for analyzing the groundwater in the Broujerd region of Iran. Eleven quality factors were obtained from DEM for mapping groundwater. A study conducted by Yadav et al. [19] highlighted a combination GIS with PCA to identify the contaminants of aquifers in India. Their findings pointed out that PCA coupled with a GIS tool provides acceptable results for the assessment of groundwater. According to Naghibi et al. [20], a groundwater quality map of Koohrang, Iran was generated using data mining, considering 13 environmental factors. They found that the integration of environmental data with data-mining approaches could provide valuable insight into the potential quality of groundwater. GIS-AHP was used by Shahid et al. [18] for assessing groundwater quality in the Western Ghats, India. The results of their study indicated that the method was approximately 85% accurate. As part of an investigation into groundwater quality in rural northwest Iran, Mosaferi et al. [21] employed PCA together with a GIS. They reported that multivariate analysis could be successfully applied to the evaluation of groundwater. Honarbakhsh et al. [11] found that the combined use of geostatistics and geographic information systems (GIS) provided acceptable results for assessing groundwater quality. Abdalla et al. [22] demonstrated that the integration of RS and GIS leads to improving the monitoring of water resources. In Pakistan, Ijumulana et al. [23] highlighted the potential risks of groundwater in GIS. They indicated that drinking water quality varied from one geological to another.
Despite the benefits of the use of GIS to investigate groundwater quality, few researchers have merged GIS and statistical models to evaluate groundwater in southern Iran. Statistical analysis coupled with GIS has not been utilized for assessing the relationships between soil, topographical attributes, and groundwater. Also, the effects of geological terrain attributes on the quality of groundwater have not been adequately examined. The aims of this therefor research were: (1) to test the statistical attitudes (PCR and MLR) for evaluating the groundwater quality in Firuzabad, Iran, which is a main water source for drinking and irrigating purposes water. In addition, we attempted: (1) to identify soil parameters and topography attributes that can be used to assess groundwater quality, and (2) to evaluate the potential of a GIS application to analyze the spatial analysis of groundwater potentials.
The study area is situated at a latitude and longitude of 28°52′ to 28°47′ N and 52°24′ to 52°39′ E in Firuzabad, Fars, Iran (Fig 1). The aquifer acreage is 282.5 km 2 (Fig 1) of Firuzabad plain with an area of 545 km 2 . Firuzabad Aquifer is the main source of drinking water for the city of Firuzabad with 121,000 and approximately 25 villages. Furthermore, it provides irrigation water for agricultural purposes. The climate at the region is semi-arid with 291.7 mm of rainfall and 17.5°C. The wettest months are December, January, and February. Summer months are marked by high temperatures, while winter months are marked by relatively low temperatures. An altitude range of 1124–2721 m was observed. At the center of the area, there are agricultural lands, whereas the elevated areas are dominated by mountains. Alluvial deposits dated between Q1 and Q3 are dominated by dolomite and calcite [4, 11]. The marls and limestones in both the Hormuz and the Asmari are also soluble [11]. The Inceptionsol, the Entisol, and the Aridisol (Soil Taxonomy) are three types of soil. The water was found 45 meters below ground level [24].