Nationwide prediction of drought conditions in Iran based on remote sensing data

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the country. Satellite-based remote sensing dataa^that are freely availablea^give information on vegetation conditions and land cover. In this paper, we constructed artificial neural network to model (and forecast) drought conditions based on satellite imagery. To this end, standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and temperature condition index (TCI) were extracted from NOAA-AVHRR images. The model received these features as input and outputted the SPI value (or drought condition). Applying the model to the data of stations for which the precipitation data were available, we showed that it could forecast the drought condition with an accuracy of up to 90 percent. Furthermore, TCI was found to be the best marker of drought conditions among satellite-based features. We also found multilayer perceptron better than radial basis function networks and support vector machines forecasting drought conditions

Multicriteria Decision Making for Healthcare Facilities Location with Visualization Based on FITradeoff Method

This paper proposes an application of the Flexible Interactive Tradeoff (FITradeoff) method for siting healthcare facilities. The selection of the location of complex facilities, as hospitals, can be considered as a multidimensional decision problem for the several issues to be taken into account and, moreover, for the variety of stakeholders that should be involved. The case study under investigation is the location of “La Città della Salute”, a new large healthcare facility in Lombardy Region (Italy). Starting from a cross disciplinary literature review, a multidimensional evaluation framework has been defined and applied to the case study by considering the point of view of one Decision Maker (DM). The application shows that a smaller effort is required from the DM using the FITradeoff method

Approaching the Location of Healthcare Facilities: How to Model the Decision Problem

The management of health policies is characterised by multi-level hierarchies and actors. Given the presence of several and sometimes conflictual needs and expectations elicited by stakeholders involved and the complexity of the decision problem concerning the location of healthcare facilities, a multi-methodological evaluation framework is proposed within this contribution. The process is based on the four traditional stages of the Multi-Criteria Decision Analysis (MCDA), recognised as the most critical in the context of the location of healthcare facilities, namely stakeholder analysis, criteria definition, weights assignments and aggregation procedure. Since several methodologies have to be combined, the proposed framework is conceived as an iterative and flexible approach where each phase can be reviewed when necessary. The methodology proposed is tested on a case study located in the Municipality of Milan, Italy