Modelling and predicting the spatial dispersion of skin cancer considering environmental and socio-economic factors using a digital earth approach

Masoumi, Zohreh; Genderen, J.L. van; Mesgari, Mehran

doi:10.1080/17538947.2018.1551944

Cited by 6 publications

(5 citation statements)

References 43 publications

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“…This process uses a set of predefined analytical methods and processes to analyze information related to the existing spatial relationships (Ding et al, 2014). The general purpose of spatial modeling is to study how place‐related phenomena affect each other in the real world (Fotheringham & Wegener, 1999; Masoumi et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Currently, spatial technology as an emerging technology for disaster management is developing rapidly. Given that disasters are fundamentally spatial, GIS plays a critical role in disaster management (Masoumi et al, 2018). GIS‐based susceptibility assessment methods could be adapted to urban flood‐prone areas and upgraded by advances in crowdsourcing using geospatial data creation and collection (Sharma et al, 2016; Ţîncu et al, 2018; Zeng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Flood susceptibility assessment for ungauged sites in urban areas using spatial modeling

Masoumi

2021

J Flood Risk Management

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In urban areas, flood susceptibility assessment is of special importance because of the high settlement population, properties, and infrastructures. Geospatial information science (GIS) provides a tool for investigating flood susceptibility based on several aspects including economic damages and critical infrastructures in cities. This study aims to provide a tool based on GIS analyses to support decision‐makers in identifying flood hazards in urban areas, in which previous flood data, flood causative factors, and urban infrastructure data are not adequately available. To assess flood susceptibility in the study area, the related spatial high‐resolution data were produced. Then, flood zones were estimated for different recurrence intervals using meteorological data. Finally, susceptibility was assessed in urban areas for different urban infrastructures using GIS modeling. The model was designed based on the assumption that any critical urban infrastructure, such as power towers, was affected by flood zones and, in addition to flooding, could cause hazards by itself. Moreover, five scenarios were defined to calculate susceptibility when in the problematic locations of the floodway. DoAsb Floodway was chosen as a case study located in Zanjan city, northwest of Iran. The results indicated the high‐susceptible areas around the floodway. Moreover, the flood susceptibility level for each urban infrastructure in the study area was calculated and classified into five classes from low susceptible to very high susceptible. Also, the results of the five scenarios showed if some parts of the floodway had problems, the susceptibility rate would be high. The generated flood susceptibility maps of this model can be used to plan suitable programs in order to avoid flood damage and ensure public safety.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood susceptibility assessment for ungauged sites in urban areas using spatial modeling

Masoumi

2021

J Flood Risk Management

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“…In contrast, ML techniques can be used to learn and predict the association of the landslides' positions and their associated causative factors (e.g., [15][16][17]). ML techniques do not need statistical assumptions and also can model the nonlinear character of landslides [9,12]. Therefore, ML, especially DL techniques, have been attracting more attention in landslide studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous research has reported the ability of machine learning (ML) methods [6,11], and recently deep learning (DL) methods as a subclass of ML, in recognizing susceptible areas to landslides in different regions [12,13]. It has been indicated that the performance of ML methods could be different with respect to the region, input influencing factors, the accuracy of input data, etc.…”

Section: Introductionmentioning

confidence: 99%

A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

et al. 2022

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Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.

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“…The relative risk of skin cancer varies in different cli-mates and depends on geographical regions (17,18). The difference in the incidence of this disease is due to the geographical features of the regions which influence the type of clothing, nutrition, and activities of the people (19,20).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Environmental Factors on Skin Cancer

et al. 2022

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Background: Skin cancer, as one of the most common cancers, is partly associated with environmental conditions. Objectives: The main purpose of this study was to investigate the effect of environmental factors on the incidence of skin cancer. Methods: The dataset contains the age-standardized incidence rate (ASR) of skin cancer in all provinces of Iran during a six-year period of time. Also, there were meteorological variables for a 29-year period for the provinces. We used the mean of meteorological variables for the 29-year period for each province as predictors. The ASR of skin cancer in each province in six years was considered as the response variable. Also, gender, latitude, altitude, wind speed, relative humidity, cloudy hours duration, and maximum air temperature in each province were considered independent variables. A mixed effects polynomial regression model was applied to data analysis. Results: Based on the applied model, the trend of ASR of skin cancer in different provinces was statistically significant (P < 0.001). The ASR of skin cancer for males was more than the females (P < 0.001). Furthermore, the positive linear effect of latitude was significant (P = 0.002). However, the quadratic term showed a deceleration in the growth rate of skin cancer incidence with increasing latitude. Furthermore, there was a positive significant impact of altitude on skin cancer incidence (P = 0.045). Conclusions: Health policymakers in their prevention planning should consider geographical conditions as influencing factors on skin cancer incidence. Moreover, men should be given more attention as the main group at risk for this cancer.

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Modelling and predicting the spatial dispersion of skin cancer considering environmental and socio-economic factors using a digital earth approach

Cited by 6 publications

References 43 publications

Flood susceptibility assessment for ungauged sites in urban areas using spatial modeling

Flood susceptibility assessment for ungauged sites in urban areas using spatial modeling

A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

The Impact of Environmental Factors on Skin Cancer

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