查看更多>>摘要：? 2021 Elsevier B.V.The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods comprised Random forest (RF), and Cubist. In this respect, a combination of different input environmental variables (remote sensing data, topographic attributes, thematic maps and soil properties) were used in modeling under four scenarios (I: remote sensing data (RS); II: RS + topographic attributes resulted from digital elevation model (DEM); III: RS + topographic attributes + thematic maps; IV: RS + topographic attributes + thematic maps +soil properties). The maps of Euclidean distance from mines and roads as well as the geology map have been used as thematic maps. A total of 346 soil samples were taken using stratified random sampling from the surface layers (0–20 cm depth) of the studied area and selected heavy metals (Ni, Fe, Cu, Mn), and soil properties were measured in the laboratory. RF and Cubist models were used to predict soil heavy metals in four scenarios. The results indicated that the best prediction accuracy was achieved for the fourth scenario (IV) when all input variables were combined to predict selected heavy metals. Moreover, two models showed different capability for various metals. According to our results, the random forest model had a high accuracy in predicting Ni (R2 = 0.67) and Cu (R2 = 0.60), In contrast, the Cubist model had a higher accuracy in predicting Mn (R2 = 0.55). For predicting Fe, both models provided a similar accuracy (R2 = 0.73). This study proved the high capability of machine learning methods to use easily available environmental data to predict studied heavy metals in the large scale that are essential for decision making in sustainable management in agricultural and environmental concerns.
查看更多>>摘要：? 2021 Elsevier B.V.Comprehensive hydrogeochemical studies have been conducted in the Campi Flegrei volcanic aquifer since late 20th century due to the volcanic unrest. In the last decade, groundwater samples were grouped based on the dominant anion species (i.e. bicarbonate, sulfate and chloride) to explain the general hydrogeochemical processes. In this article, 44 groundwater samples are collected from Campi Flegrei aquifer to geochemically and spatially capture the main characteristics of the groundwater body. The hierarchical clustering algorithm is then performed on proportion of bicarbonate, sulfate and chloride, and the optimum number of clusters are determined regarding the results of deep hydrogeochemical investigations published in the past. The collected samples are categorized in the following groups: (1) bicarbonate-rich groundwater; (2) chlorine-rich groundwater; (3) sulfate-rich groundwater; and (4) mixed groundwater. The first group (As = 158.2 ± 169 μg/l, electric conductivity = 1,732.1 ± 1,086 μS/cm and temperature = 25.6 ± 8 °C) is mainly derived from poor arsenic meteoric water, but there is significant thermal/seawater contribution in the second one (As = 1,457.8 ± 2,210 μg/l, electric conductivity = 20,118.3 ± 11,139 μS/cm and temperature = 37.1 ± 20 °C). Interaction of the bicarbonate-rich groundwater and hydrothermal vapors gives rise to the sulfate-rich groundwater (As = 847.2 ± 679 μg/l, electric conductivity = 3,940.0 ± 540 μS/cm and temperature = 82.8 ± 3 °C) around Solfatara volcano. The mixed groundwater (As = 451.4 ± 388 μg/l, electric conductivity = 4,482.9 ± 4,027 μS/cm and temperature = 37.1 ± 16 °C) is observed where the three main groundwater groups undergo a mixing process, depending on the hydrogeology of the volcanic aquifer. Contrary to the bicarbonate- and sulfate-rich groundwater, the chlorine-rich and mixed groundwater generally occurs at low piezometric levels (approximately <1 m above sea level) near the coastline. The hierarchical cluster analysis provides more information about the volcanic aquifer, particularly when compositional data analysis is applied to study hydrogeochemistry of the homogeneous groundwater groups and to uncover the relationships between variables. Addressing compositional nature of data is recommended in the future studies for developing new tools that help deeper understanding of groundwater evolution in volcanic aquifers and identifying promising precursors of volcanic eruption.
查看更多>>摘要：? 2021 Elsevier B.V.In this paper, in order to reveal the regional geochemical patterns of regularly sampled stream sediment data, we have employed the K-means and self-organizing map (SOM) as clustering methods in the Moalleman district, northeast Iran. Initially, a set of analyzed elements of geochemical data was subjected to isometric log-ratio (ilr) transformation to address the closure problem related to geochemical data, then, ordinary principal component analysis (PCA) was utilized for recognizing the internal relations between selected elements (As, Au, Cu, Pb, Sb and Zn). Subsequently, the K-means and SOM as unsupervised clustering methods were applied based on PC1 (Cu-Pb-Zn aggregation) and PC2 (Au-As-Sb aggregation) to distinguish different populations of multi-element geochemical indicators. In this regard, Silhouette Width (SW) was implemented for computing the optimal cluster number in K-means clustering method. In the next step, due to the presence of numerous copper mineral deposits/occurrences in the study area, we opted to implement the supervised SOM on ilr-transformed values of Cu-Pb-Zn elements for delineating high anomalous zones. For this purpose, a confusion matrix based on training and out-of-bag (OOB) data was developed for the supervised SOM model and the results indicated the accuracy of 96.27% and 94.26%, respectively. Moreover, success-rate curves were used for assessing the overall performance of K-means and SOM (unsupervised and supervised) models. Experimental outcomes represented the superiority of SOM models (especially the supervised SOM) over K-means in delineating the geochemical anomaly targets which can be used as an effective and powerful tool for discovering the complex patterns among variables in exploratory geochemical data.
NSTL
Elsevier