Surface water quality analysis using multivariate statistical techniques: a case study of Fars Province rivers, Iran

Ghaemi, Zeynab; Noshadi, Masoud

doi:10.1007/s10661-022-09811-1

Cited by 13 publications

(5 citation statements)

References 97 publications

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“…This allows for the cultivation of a variety of agricultural crops in this province under various climatic conditions 43 . The variation of topography in Fars has had an impact on the types of ora that grow there 44 . Therefore, the elevation range of the study area is 270 to 3491 m. The mean annual temperature ranges from 3 to 19°C, and the mean annual rainfall ranges from 141 to 637 mm.…”

Section: Study Areamentioning

confidence: 99%

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Dastres

Bijani

Naderi

et al. 2023

Preprint

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Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.

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Section: Study Areamentioning

confidence: 99%

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Dastres

Bijani

Naderi

et al. 2023

Preprint

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“…PCA is one of the multivariate statistical methods used for analysing the environmental issues, for instance pollution analysis, spatial-temporal change in land resource quality and so on (Yang et al, 2020). It is applied commonly to establish the relation among the original water and soil parameters by transforming them into independent component (Fu et al, 2020;Ghaemi & Noshadi, 2022). Among a vast obtained data, reduction of factors is crucial to understand which among them are influencing the water and soil quality majorly.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…The water quality parameters such as pH, EC, Salinity and nutrient metals like Ca, Mg, K, and Na are clear indications of increased drought and salt contamination in the area. Increased salinity can make the soil dispersive and nutrient deficient (Ghaemi & Noshadi, 2022). Most crops need a slightly acidic medium for nutrient absorption (Tsymbarovich et al, 2020).…”

Section: Soil Water Interaction Based On Soil and Water Quality Analysismentioning

confidence: 99%

“…Rainfall is naturally acidic with a pH of 5 to 6 (Paul et al, 2019). The high intensity rainfall for a prolonged period causes weathering of surface soil and dilution of ions in surface water (Fu et al, 2020;Ghaemi & Noshadi, 2022). The precipitation water percolates and reacts with carbon dioxide generated in microbial processes and air trapped in pores in summers to form carbonic acid (Mali et al, 2020;Rao et al, 2021).…”

Section: Monsoonmentioning

confidence: 99%

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Water induced surface erosion risk assessment in Jumar agricultural watershed, Jharkhand, India using multivariate statistics and GIS

Pandey¹,

Kumari²

2023

Preprint

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The state of Jharkhand suffers immensely from water-induced surface erosion causing loss of socio-economic Jumar watershed of Ranchi district, Jharkhand was selected for a study period of 2020 and 2021 to analyse climate change, seasonal change in quality of water and soil resources, hydro-geomorphometry and land cover land use (LULC) using GIS and multivariate statistics. The water quality analysis revealed the presence of higher content of presence of metals such as Ca, Mg, K, and Na, higher alkalinity, TDS, and turbidity in monsoon and post-monsoon months of 2021. The soil quality analysis showed that the region has excessive carbonate salts with dominant presence of metal such as calcium, magnesium and sodium, disturbing the soil structure and lowering its infiltration capacity, promoting nutrient deficiencies, and leading to possible metal contamination in few years. Lack of vegetation covers adds to surface erosion under high intensity rainfall (1685-2083mm). The correlation analysis was done seasonally for both soil and water quality revealing the strong influence of metal ions with pH, alkalinity, salinity, and TDS, showing a prominent silicate weathering and ion exchange process indicating prominent surface erosion in watershed. The PCA on water and soil quality data showed that salinity, TDS, Alkalinity, Hardness and EC are the principal components. The morphometric study showed that the Jumar watershed has rolling and slightly undulating terrain, with mild slope and dendritic stream pattern of 4th order stream. Low basin relief and drainage density, compactness constant (0.00192), Ruggedness number (0.0609), and basin relief ratio (0.00552) indicated the dominance of low stream orders (1st ) in the area pointing to low potential energy in surface flow. The hydrological indices indicated that the watershed is low risk of gully formation in lowlands. Through the application of other GIS based rainfall, LULC and spectral indices (NDMI, MSI, SAVI and BSI) it was found that there has been a huge increase in urbanization, barren land, sparse vegetation cover, and immense water stress especially in summer and winter season of 2020 and 2021 in the watershed. The Lack of water conservation and traditional agriculture using agrochemicals is very prominent in the watershed, hence an immediate need for water and soil quality management is required.

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“…As a reference many publications reflect the use of multivariate statistical analysis in the evolution of salinization conditions in coastal areas (Ferchichi et al 2018;Soltani and Mellah 2023), in the study of water quality (Kantiranis et al 2017;Wang et al 2018;De Andrade Costa et al 2020;Varol 2020;Ghashghaie et al 2022;Ghaemi and Noshadi 2022;Wali et al 2022), and in the variation of piezometric levels (Machiwal, and Singh 2015;Adamovic et al 2016;Rabiei et al 2022). These techniques thus allow the analysis of relationships between variables in a comprehensive way and their quantification (Shiker 2012;Javadinejad et al 2019a).…”

Section: Introductionmentioning

confidence: 99%

Trend detection and depletion effects evidence in time series of groundwater levels in the southern sector of the left bank of the Tagus-Sado Basin (Portugal, Iberian Peninsula)

Ferreira Branco,

Barbosa,

Matos

2024

Sustain. Water Resour. Manag.

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In the last 20 years in Portugal, water resources have been affected to the point that water storage has decreased by 20% since 2000. Creating strategies to manage water resources requires a comprehensive understanding of the factors influencing water storage and their effects over time. This study is focused on the evolution of Groundwater Deep Levels (GDL) by applying a two-phase trend analysis methodology to examine the dynamic changes in GDL within a series of monitoring wells located in the Central and Southern sectors of the Left Bank of the Tagus-Sado Cenozoic age Basin, situated in Portugal In the initial phase of trend analysis, Factorial Analysis of Mixed Data (FAMD) was employed and posteriorly the Hierarchical Classification Analysis (HCA). These techniques enabled us to identify distinct GDL trend profiles and generate interpretative maps illustrating their spatial distribution. In the second phase, the non-parametric Mann–Kendall Analysis (MKA) and Innovative Trend Analysis (ITA) were applied, allowing for a quantified confirmation of the different trend profiles previously detected. These techniques allowed the identification of positive and negative hydrodynamic trends in distinct sections of the Basin. In the SE sector they are characterized by a significative increase of GDL associated with overexploitation and in the Central sector with a decrease of GDL. Nevertheless, significant depletion effects can result from natural factors such as prolonged droughts, and in certain regions, changes in geological and hydrothermal dynamics, such as Alpine-age faults, graben, and horst structures, may account for these alterations.

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Surface water quality analysis using multivariate statistical techniques: a case study of Fars Province rivers, Iran

Cited by 13 publications

References 97 publications

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Water induced surface erosion risk assessment in Jumar agricultural watershed, Jharkhand, India using multivariate statistics and GIS

Trend detection and depletion effects evidence in time series of groundwater levels in the southern sector of the left bank of the Tagus-Sado Basin (Portugal, Iberian Peninsula)

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