3 research outputs found
Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran
10.2166/nh.2019.090Hydrology Research511127-14
Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran
Abstract
In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city
Cancer incidence in Iran in 2014: Results of the Iranian National Population-based Cancer Registry
Background: We aimed to report, for the first time, the results of the Iranian National Population-based Cancer Registry (INPCR) for the year 2014. Methods: Total population of Iran in 2014 was 76,639,000. The INPCR covered 30 out of 31 provinces (98 of total population). It registered only cases diagnosed with malignant new primary tumors. The main sources for data collection included pathology center, hospitals as well as death registries. Quality assessment and analysis of data were performed by CanReg-5 software. Age standardized incidence rates (ASR) (per 100,000) were reported at national and subnational levels. Results: Overall, 112,131 new cancer cases were registered in INPCR in 2014, of which 60,469 (53.9) were male. The diagnosis of cancer was made by microscopic confirmation in 76,568 cases (68.28). The ASRs of all cancers were 177.44 and 141.18 in male and female, respectively. Cancers of the stomach (ASR = 21.24), prostate (18.41) and colorectum (16.57) were the most common cancers in men and the top three cancers in women were malignancies of breast (34.53), colorectum (11.86) and stomach (9.44). The ASR of cervix uteri cancer in women was 1.78. Our findings suggested high incidence of cancers of the esophagus, stomach and lung in North/ North West of Iran. Conclusion: Our results showed that Iran is a medium-risk area for incidence of cancers. We found differences in the most common cancers in Iran comparing to those reported for the World. Our results also suggested geographical diversifies in incidence rates of cancers in different subdivisions of Iran