Elevation is a basic information of the earth, and different elevation models are provided to better understanding the earth and its different functions. However, it is not always possible to conduct a comprehensive survey in big areas and calculate all surface points. The best way is survey some points, then the elevation estimation is done using these points in each part of study area. The purpose of this paper is to use interpolation methods to estimate elevation. In such cases, different methods are used to interpolate and estimate points with an uncertain height. In this paper, the three usual methods are chosen and introduced then their performance are compared. These methods including: Inverse Distance Weighting (IDW), the Krige method or Kriging, and Artificial Neural Network (ANN). The results show that Artificial Intelligence with RMS = 5.9m is better in compare to Kriging with RMS = 7.2 and IDW with RMS = 9. The obtained result presents that in despite of its convenience, ANN provides DEMs with minimum errors.

Objective: Early recognition of autism is important, but diagnosis age varies among children. Recent studies have aimed to identify factors affecting age of diagnosis and several studies have attempted to explore geographic variation in age at diagnosis of autism. However, there is a lack of research examining geographic variations with multiple models to find whether geographic differences can be explained by risk factors such as socioeconomic status and differences in child characteristics. This study aimed to address this gap of knowledge by comparing age at diagnosis of autism between the group of people living in the center of the province and the group of people living in the rest of the province, considering potential medical and socioeconomic confounders. Method: The study population consisted of 50 autistic children born in East Azerbaijan Province between 2004 and 2016. Initially, univariate testing by ANOVA was performed to identify family and individual factors contributing to differences in age at autism diagnosis. Following this, the association between living in the center of the province and age at diagnosis in univariate and multivariate analyses was examined. Results: Results from the initial univariate analysis indicate a significant association between living in the center of province and early diagnosis. However, inclusion of possible confounders in multiple model illustrates that these geographical disparities in age at diagnosis can be explained by differences in socioeconomic and medical status. Conclusion: Although geographic variation in age at diagnosis of autism was observed, analyses show that differences in individual and family-level factors may contribute to geographic differences. In this study, most of the observed variation was accounted for by family-level factors rather than geographic policies. Findings prove that multiple strategies are required to identify targeted interventions and strategies.