Principal Component Analysis (PCA)–Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan

Akbar, Tahir Ali; Javed, Azka; Ullah, Siddique; Ullah, Waheed; Pervez, Arshid; Akbar, Raza Ali; Javed, Muhammad Faisal; Mohamed, Abdullah; Mohamed, Abdeliazim Mustafa

doi:10.3390/su142114572

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…Many researchers have applied IDW to interpolate various parameters such as PM2.5 concentrations [25], soil Cadmium [26], and rainfall [27]. Moreover, the researchers used IDW to interpolate the values such as [28][29][30]. The Spline method utilizes a mathematical function to estimate values while minimizing surface curvature.…”

Section: Spatial Interpolation Techniquesmentioning

confidence: 99%

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

Panyadee,

Champrasert

2024

Sustainability

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Floods cause disastrous damage to the environment, economy, and humanity. Flood losses can be reduced if adequate management is implemented in the pre-disaster period. Flood hazard maps comprise disaster risk information displayed on geo-location maps and the potential flood events that occur in an area. This paper proposes a spatiotemporal flood hazard map framework to generate a flood hazard map using spatiotemporal data. The framework has three processes: (1) temporal prediction, which uses the LSTM technique to predict water levels and rainfall for the next time; (2) spatial interpolation, which uses the IDW technique to estimate values; and (3) map generation, which uses the CNN technique to predict flood events and generate flood hazard maps. The study area is Chiang Mai Province, Thailand. The generated hazard map covers 20,107 km2. There are 14 water-level telemetry stations and 16 rain gauge stations. The proposed model accurately predicts water level and rainfall, as demonstrated by the evaluation results (RMSE, MAE, and R2). The generated map has a 95.25% mean accuracy and a 97.25% mean F1-score when compared to the actual flood event. The framework enhances the accuracy and responsiveness of flood hazard maps to reduce potential losses before floods occur.

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Section: Spatial Interpolation Techniquesmentioning

confidence: 99%

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

Panyadee,

Champrasert

2024

Sustainability

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“…Additionally, it should be noted that the primary origins of Ni, Co, and Cr can be attributed to natural sources [51,58]. Principal component analysis (PCA) has been implemented as a technique to assess statistical elements related to soil contamination, resource exploitation, protection, and degradation of the environment [61,62]. PCA is a multivariate statistical methodology performed to identify and classify comparable variables based on their relationships.…”

Section: Heavy Metal Concentrationsmentioning

confidence: 99%

Risk Assessment of Heavy Metals in Sediment Samples from the Mae Chaem River, Chiang Mai, Thailand

Kawichai,

Prapamontol,

Santijitpakdee

et al. 2023

Toxics

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Heavy metals are significant environmental pollutants that are recognized as posing a potential health hazard to human beings. We investigated the concentrations of the heavy metals As, Cd, Cr, Cu, Ni, Pb, and Zn in surface sediments collected from the Mae Chaem River in Chiang Mai, Thailand, during the dry season in 2021. The mean concentrations of heavy metals in sediments were, in decreasing order, Zn > Cr > As > Pb > Ni > Cu > Cd. The mean values of As, Cd, Cr, and Cu were determined to be 32.5 ± 18.3, 0.33 ± 0.07, 45.8 ± 11.9, and 21.9 ± 7.42 mg Kg−1, respectively. These levels are higher than their standard levels in Thailand, namely 10.0, 0.16, 45.5, and 21.5 mg Kg−1, respectively. Principal component analysis (PCA) revealed that the primary origins of heavy metal contamination are predominantly attributed to residential settlements and agricultural areas. The hazard quotient (HQ) was used to estimate the non-carcinogenic risk of exposure to heavy-metal-bound surface sediments for both children and adults. The results showed that the HQ values for both groups were less than 1.0 (HQ < 1.0), indicating no risk. Moreover, assessment of the long-term risk for ingestion of toxic metals indicated no risk (<10−6) based on the lifetime cancer risk (LCR). However, the LCR values of As and Cr were 5.3 × 10−6 and 2.5 × 10−6, respectively, demonstrating the most elevated LCR among the hazardous metals in terms of children’s exposure. Therefore, it is possible that children living in agricultural areas and participating in activities around the study area may be exposed to elevated concentrations of As and Cr.

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“…Multivariate statistical analysis is applied to the evaluation of water quality and the analysis of pollution sources in urban lakes [28]. Akbar et al used PCA and GIS to analyze risk factors affecting groundwater quality [29].…”

Section: Introductionmentioning

confidence: 99%

Research on the Algal Density Change Features of Water Bodies in Urban Parks and the Environmental Driving Factors

2022

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Water bodies in urban parks are important for scenic and recreational areas, yet algal bloom problems seriously affect public use; therefore, it is important to study the features of algal density (AD) changes and environmental driving factors (EDFs) for water body management. In this study, five scenic water bodies in urban parks of Xinxiang City are taken as the objects for studying the AD and nine environmental indicators from March to October 2021, in accordance with time-series monitoring. The features of AD change in different layers of the water bodies are analyzed, and the main environmental impact factors of AD changes are screened by Pearson correlation analysis and principal components analysis (PCA), with main EDFs further extracted according to multiple linear regression analysis (MLRA), and multiple regression equation established. According to the data, ADs at different depth layers increase at first and then decrease with time, reaching the peak in August. According to the PCA, three principal components (PCs) are extracted in the 0.5 m and 1.0 m water layer, the variance contribution of which is 87.8% and 87.3%, respectively. The variance contribution of four PCs extracted in the 1.5 m water layer is 81.7%. After MLRA, it is found that the main EDFs of algal density in the 0.5 m water layer are electrical conductivity (EC), dissolved oxygen (DO), and water temperature (WT), in the 1.0 m water layer are WT and DO, and in the 1.5 m water layer are WT, DO, total nitrogen (TN), and EC. Generally speaking, WT and DO are decisive factors affecting AD. The EDFs’ leads to the AD changes in different water layers are analyzed, and it is proved that stratification occurs in scenic water bodies in urban parks. This study is expected to provide basic data and a theoretical basis for ecosystem system protection and water quality management of scenic water bodies in urban parks.

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Principal Component Analysis (PCA)–Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan

Cited by 7 publications

References 58 publications

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

Risk Assessment of Heavy Metals in Sediment Samples from the Mae Chaem River, Chiang Mai, Thailand

Research on the Algal Density Change Features of Water Bodies in Urban Parks and the Environmental Driving Factors

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