AGIS method based on spatial autocorrelation analysis used to identification and ranking of thoron (220Rn)concentration. Spatial radiation patterns are analyzed using Moran's I statistic. Getis-Ord Gi* is utilized to locate clusters of high and low measurements and create a map of thoron hot spots. One hundred schools in the center of Najaf City were examined for thoron using CR-39 detectors (produced from Track Analysis Systems Ltd., UK)  for this research. Average thoron levels were 2.99 Bq/m3, with a range of 9.00 Bq/m3 to 0.22 Bq/m3. The radiation levels found in this investigation were significantly lower than the UNSCEAR 2000 safety standards of 40 Bq/m3. Moran's, I have used it to analyze the clustering of districts across a research region and to measure the spatial distribution of data. Getis-Ord Gi* statistics were used to identify cold and hot spots within the research area. Thoron concentrations were shown to have insignificant spatially random distribution patterns, as demonstrated by Global Moran's I. (Moran’s I =0.28, p-value=0.24).

The development of suitability species models look for the availability to growth in a study area. These models can be used for different targets. In this research, a suitability model of Eucalyptus has been developed to soils contaminated by trace elements management. Guadiamar Green Corridor has been selected due to the huge data available regarding trace elements, forestry species and so on. Logistic regression (LR) and Random Forest (RF), as popular machine learning model, were applied in a geodatabase from Guadiamar Green Corridor with more of 20 years of data. This database is composed by soil physical and chemical variables, climate (temperature min and max, annual precipitation), forestry species. The results show the poor performance of LR and RF applied directly over the unbalanced training set. However, when Up-sampling or SMOTE are applied, both procedures improve its sensitivity, however, RF show more improve that LR. The methodology applied can help to determine the potential distribution of Eucalyptus in similar Mediterranean areas and extended to different areas according to Soil, Climate and Trace Elements data. Finally, the models developed under this research work can be used to reduce human and environmental health by trace elements taking into account local conditions but also climate change scenarios.

The impact of global warming is climate change which affects elements of society. This condition causes a decrease in the level of community welfare and increases the level of community vulnerability. Some climate change impacts are floods, droughts, landslides, and shoreline changes. In this study, we will focus on landslides. Landslides are among the most dangerous natural disasters that often occur in mountainous areas, especially during the rainy season. Various factors influence events involving landslides. This study aims to utilize GIS to identify landslide-prone areas in Padang. The method used in this study is the Zuidam and Concelado criteria overlay method for the level of landslide hazard and the broken method (jenks). The natural break (jenks) classification method reduces within-class variation and maximizes between-class variation. This study shows that the level of landslide vulnerability in Padang City is low, with a total area of 288854.38173 ha with a percentage of 42.21%. We need to consider more factors and experiment with training and validating data in more detail to gain insight into the physical contributions of the factors to landslide occurrences.