Applicability of MIKE SHE to Simulate Hydrology in Argesel River Catchment

AbstractA fully distributed, physically-based hydrologic modelling system, MIKE SHE, was used in this study to simulate surface flow as runoff and subsurface flow drainage routed through tile drainage infrastructure within the Argesel River watershed. MIKE SHE, Système Hydrologique Européen, is a sub-model under the collection of models within the MIKE framework from the Danish Hydraulic Institute (DHI). It covers the major processes in the hydrologic cycle and includes process models for evapotranspiration, overland flow, unsaturated flow, groundwater flow, and channel flow and their interactions. The study's focus was the development of a MIKE SHE model based on available data that can be used in land use management decisions and assessment of hydrological mitigation measures. Sensitivity analyses show that a few individual parameters play an important role in the hydrologic modelling. Vegetation parameters and the root depth as well as empirical parameters influence evapotranspiration, transpiration and recharge, in the unsaturated zone, the type of the soil has an effect on the infiltration/evapotranspiration and recharge functions and at the saturated zone level, the hydraulic conductivity of the matrix represents the dominant parameters

Development of a downstream emergency response service for disaster hazard management based on Earth observation data

The number of hydrological (flood, mass movement), meteorological (tropical storm, extratropical storm, convective storm, local storm), climatological (extreme temperature, drought, wildfire) and geophysical (earthquake, tsunami, volcanic activity) events continue to increases in the last decades at global level. According to different research, statistics and databases (UNISDR, EM-DAT) floods are the most frequent in the last decades worldwide and especially in Romania. On the other hand, the probabilistic hazard results for Romania indicate that, in the future, the highest damages will be produced by floods and earthquakes. In this context, it has become necessary to develop an emergency response service. The emergency service, named GEODIM, integrates the GIS geodatabases: roads, rivers, administrative units, land cover/land use, satellite data (optical and synthetic aperture radar), in-situ measurements, in order to support the disaster management. The Earth Observations data offers the capabilities to monitor the disasters at a large scale, being able to identify areas where the events are not in-situ observed or to monitor large vulnerable areas potentially affected by disasters. The paper presents the downstream emergency response service for disaster hazard in Romania, based on Earth Observation data and other geo-information information

A comparison of waste education in schools and colleges across five European cities

PeerReviewe

Food Recognition and Food Waste Estimation Using Convolutional Neural Network

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation