DEVELOPMENT AND EVALUATION OF AN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR THE CALCULATION OF SOIL WATER RECHARGE IN A WATERSHED

Modeling of groundwater recharge is one of the most important topics in hydrology due to its essential application to water resources management. In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) method is used to simulate groundwater recharge for watersheds. In-situ observational datasets for temperature, precipitation, evapotranspiration, (ETo) and groundwater recharge of the Lake Karla, Thessaly, Greece watershed were taken into consideration for the present study. The datasets consisted of monthly average values of the last almost 50 years, where 70% of the values used for learning with the rest for the testing phase. The testing was performed under a set of different membership functions without expert’s knowledge acquisition and with the support of a five-layer neural network. Experimental verification shows that, the 3-3-3 combination under the trapezoid membership function with the hybrid neural network support and the 2-2-2 combination under the g-bell membership function with the same neural network support perform the best among all combinations with RMSE 4.78881 and 4.12944 giving on average 5% deviation from the observed values

MODELING OF HYDROLOGICAL AND ENVIRONMENTAL PROCESSES THROUGH OPENMI AND WEB SERVICES

Integrated collaborative modeling has been proven lately to be the most accurate computer methodology that allows modelers to scrutinize the environmental processes using a holistic approach. Due to the dynamic and interdependent nature, such processes involve the interlinking of hydrological, meteorological, environmental, ecosystems and socioeconomical characteristics. In this paper we deal with the development and the integration of a collaborative system of models devoted to the water quantity and quality monitoring, and also to the management of water resources in a watershed. The system is also tailored by a socio-economical study that highlights the impact of the aforementioned management to the local community of the region under study. Models that integrate the collaborative system need to be coupled so that to run simultaneously under the spatial and temporal synchronization condition. To achieve such a simultaneous synchronization, the Open Modeling Interface, (OpenMI) is invoked. The system has been applied and tested to the Lake Karla watershed in Thessaly region, Greece. However due to the loose integration methodology used for its development and to its open ended property, the system can be easily parametrized to offer such an analysis on other similar case studies. An extension to the OpenMI standard provides the remote simultaneous run of models using web services and allowing the development of a cloud repository of models for future use

Comparing social media and Google to detect and predict severe epidemics

Internet technologies have demonstrated their value for the early detection and prediction of epidemics. In diverse cases, electronic surveillance systems can be created by obtaining and analyzing on-line data, complementing other existing monitoring resources. This paper reports the feasibility of building such a system with search engine and social network data. Concretely, this study aims at gathering evidence on which kind of data source leads to better results. Data have been acquired from the Internet by means of a system which gathered real-time data for 23 weeks. Data on infuenza in Greece have been collected from Google and Twitter and they have been compared to infuenza data from the ofcial authority of Europe. The data were analyzed by using two models: the ARIMA model computed estimations based on weekly sums and a customized approximate model which uses daily sums. Results indicate that infuenza was successfully monitored during the test period. Google data show a high Pearson correlation and a relatively low Mean Absolute Percentage Error (R=0.933, MAPE=21.358). Twitter results are slightly better (R=0.943, MAPE=18.742). The alternative model is slightly worse than the ARIMA(X) (R=0.863, MAPE=22.614), but with a higher mean deviation (abs. mean dev: 5.99% vs 4.74%)

Sentiment analysis of COVID-19 cases in Greece using Twitter data.

Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics for the last two decades, using different sources from social media to search engine records. More recently, studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of pandemics. Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the sentiment impact of COVID-19 cases in Greece in real time as related to cases. Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year and were examined towards two sentimental lexicons: one in English language translated into Greek (using the Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness, Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data. Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of COVID-19. The correlation coefficient (R2 ) for the Vader lexicon is &#8722; 0.07454 related to cases and &#8722; 0.,70668 to the tweets, while the other lexicon had 0.167387 and &#8722; 0.93095 respectively, all measured at significance level of p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the interest in COVID-19 declined after a certain time

A collaborative approach to enviromental modeling

Integrated Environmental Modeling Systems, (IEMS) are multidisciplinary systems which focus on complex environmental problems, decisions and policies. They are characterized by formulating dynamic and interdependent environmental, hydrological, climatological and sometimes socio-economical models under a unified framework that seeks to bridge the modeling, the monitoring and the decision making processes. The main concern of such systems is how to resolve various data and computational outcome inconsistencies emanated from the high computational burden, the lack of model interoperability and the model conceptualization characteristics. In this paper, we introduce an integrated modeling system that evaluates the hydrological, environmental ecosystem and socio-economic dynamics in lakes or wetland watersheds. Apart from the architectural design paradigm and the case study for the Lake Karla in Thessaly, Greece provided here, we also illustrate a roadmap for methodological planning, implementing, monitoring and managing such systems. Finally we provide the necessary steps of an environmentally related assessment process for such systems, however future research is needed to evaluate computational performance of this loosely coupled approach. © 2014 IEEE

Integrated modeling of hydrological processes through openmi and web services

Difficulty in linking data and models across organizations is one of the barriers to be overcome in developing integrated decision-making systems since not all models exist in the same location. OpenMI is a popular standard for coupling spatially and temporarily hydrological models but it requires that all involved models exist on the same machine. In this paper we present a Web Services based collaborative framework to couple hydrological models. This is achieved by converting the interface of the OpenMI configuration to be webbased and to remotely invoke the computational engines of models. Our case study shows the remote linking of a water balance model and a reservoir model applied for the reservoir of the restored Lake Karla in Thessaly, Greece. The results show that the collaboration process is not affected by the communication overhead introduced and it is bounded by the time, space and optimization characteristics of the coupled models

Under Pressure: A Comparative Study of Botrytis cinerea Populations from Conventional and Organic Farms in Cyprus and Greece

The highly heterogeneous nature of Botrytis cinerea provides adaptive benefits to variable environmental regimes. Disentangling pathogen population structure in anthropogenic agroecosystems is crucial to designing more effective management schemes. Herein, we studied how evolutionary forces exerted in different farming systems, in terms of agrochemicals-input, shape B. cinerea populations. In total, 360 B. cinerea isolates were collected from conventional and organic, strawberry and tomato farms in Cyprus and Greece. The occurrence and frequency of sensitivities to seven botryticides were estimated. Results highlighted widespread fungicide resistance in conventional farms since only 15.5% of the isolates were sensitive. A considerable frequency of fungicide-resistant isolates was also detected in the organic farms (14.9%). High resistance frequencies were observed for boscalid (67.7%), pyraclostrobin (67.3%), cyprodinil (65.9%), and thiophanate-methyl (61.4%) in conventional farms, while high levels of multiple fungicide resistance were also evident. Furthermore, B. cinerea isolates were genotyped using a set of seven microsatellite markers (simple sequence repeat [SSR] markers). Index of association analyses (Ia and rBarD) suggest asexual reproduction of the populations, even though the mating-type idiomorphs were equally distributed, indicating frequency-dependent selection. Fungicide resistance was correlated with farming systems across countries and crops, while SSRs were able to detect population structure associated with resistance to thiophanate-methyl, pyraclostrobin, boscalid, and cyprodinil. The expected heterozygosity in organic farms was significantly higher than in conventional, suggesting the absence of selective pressure that may change the allelic abundance in organic farms. However, genetic variance among strawberry and tomato populations was high, ranking host specificity higher than other selection forces studied