Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure

Predicting the susceptibility of landslide occurrence in order to manage landslide risk in Bar Neyshabur watershed

Background and objective: Landslide susceptibility zoning using different methods is one of the landslide management strategies. The purpose of this study is to evaluate the landslides susceptibility in the Bar watershed in Khorasan Razavi province using the support vector machine (SVM) algorithm. Method: First, the landslide layer of the area was corrected through field visits and Google Earth...

Refugee Camp Economies

Machine Learning in Infrastructure Security

Extended Abstract: Democratic societies throughout the world, it appears, are facing a new type of threat dubbed "asymmetric threat." In this new threat environment the world governments are faced with a number of low-intensity conflicts c haracterized by less discriminate attacks on civilian populations, infrastructures and the like. For the purposes of this paper, an asymmetric war is defined...

Predicting Shallow Landslide Initiation

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever-expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km catchment a near-annual aerial p...

Predicting Phospholipidosis Using Machine Learning

Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the impor...