Large-scale Groundwater Simulation using Artificial Neural Networks in the Danube River Basin

2020 ◽  
Author(s):  
Illias Landros ◽  
Ioannis Trichakis ◽  
Emmanouil Varouchakis ◽  
George P. Karatzas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Danube River ◽  
Bayesian Regularization ◽  
Data Set ◽  
The Danube River ◽  
Precipitation Temperature ◽  
Artificial Neural ◽  
Danube River Basin

<p>In recent years, Artificial Neural Networks (ANNs) have proven their merit in being able to simulate the changes in groundwater levels, using as inputs other parameters of the water budget, e.g. precipitation, temperature, etc.. In this study, ANNs have been used to simulate hydraulic head in a large number of wells throughout the Danube River Basin, taking as inputs, precipitation, temperature, and evapotranspiration data in the region. Different ANN architectures have been examined, to minimize the simulation error of the testing data-set. Among the different training algorithms, Levenberg-Marquardt and Bayesian Regularization are used to train the ANNs, while the different activation functions of the neurons that were deployed include tangent sigmoid, logarithmic sigmoid and linear. The initial application comprised of data from 128 wells between 1 January 2000 and 31 October 2014. The best performance was achieved by the algorithm Bayesian Regularization with a error of the order  based on all observation wells. A second application, compared the results of the first one, with the results of an ANN used to simulate a single well. The pros and cons of the two approaches, and the synergies of using both of them is further discussed in order to distinguish the differences, and guide researchers in the field for further applications.</p>

Tracking the Origin of Moisture (and Moisture for Precipitation) over the Danube River Basin through a Lagrangian Approach

2016 ◽  
Author(s):  
Danica Ciric ◽  
Milica Stojanovic ◽  
Anita Drumond ◽  
Raquel Nieto ◽  
Luis Gimeno
Keyword(s):  
River Basin ◽  
Lagrangian Approach ◽  
Danube River ◽  
The Danube River ◽  
Danube River Basin

The environmental programme for the Danube River Basin

1994 ◽  
Vol 30 (5) ◽  
pp. 135-145 ◽  
Author(s):  
D. W. Rodda
Keyword(s):  
Environmental Management ◽  
River Basin ◽  
Technical Assistance ◽  
Agricultural Practices ◽  
Danube River ◽  
The Black Sea ◽  
Diverse Range ◽  
Regional Approach ◽  
The Danube River ◽  
Danube River Basin

The Programme has the objective of providing a regional approach to environmental management in the Danube River Basin where there is great pressure from a diverse range of human activities. Serious pollution problems exist from urban populations, from industry, and from intensive agricultural practices. Although the water quality of the main Danube river is probably better than the Rhine because of its greater flow, the same is not the case in the tributaries where there the problems are more serious. A factor which makes a compelling case for a regional approach is the deterioration of the Black Sea into which the main Danube river discharges significant loads of nutrients and a range of non-degradable contaminants. The application of limited financial resources will require fine judgement about the high priority pollution sources that will lead to cost-effective improvements. This action, and other technical assistance, also requires a considerable effort to strengthen the organisations having responsibility for environmental management, and to develop effective public participation. The paper emphasises the water pollution problems in the river basin.

General Melting Point Prediction Based on a Diverse Compound Data Set and Artificial Neural Networks.

ChemInform ◽  
2005 ◽  
Vol 36 (32) ◽  
Author(s):  
M. Karthikeyan ◽  
Robert C. Glen ◽  
Andreas Bender
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Melting Point ◽  
Data Set ◽  
Artificial Neural

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

Examples of Floodplain Restoration Evaluation Studies in the Danube River Basin

2021 ◽  
Author(s):  
Francesca Perosa ◽  
Marion Gelhaus ◽  
Veronika Zwirglmaier ◽  
Leonardo F. Arias-Rodriguez ◽  
Aude Zingraff-Hamed ◽  
...  
Keyword(s):  
River Basin ◽  
Evaluation Studies ◽  
Danube River ◽  
Floodplain Restoration ◽  
The Danube River ◽  
Danube Catchment ◽  
General Benefit ◽  
Work Done ◽  
Restoration Measures ◽  
Danube River Basin

<p>Countries located in the Danube River Basin (DRB) are in danger of being affected by major catastrophic floods along the Danube and its tributaries. Floodplain restoration measures are among win-win nature-based solutions (NBS) for flood risk reduction but practitioners see their limitations in comparison to technical measures, when looking at their effectiveness and profitability. Within the framework of the EU Interreg Danube Floodplain project, this presentation shows the benefits of floodplain restoration in terms of monetized ecosystem services (ES). Our work focused on multiple ES groups for four study areas in the Danube catchment, located in Czech Republic, Romania, Serbia, and Slovenia. This was done with the help of stakeholder engagement, hydrodynamic models results, and the Toolkit for Ecosystem Service Site-Based Assessment (TESSA). Moreover, the approach was complemented with alternative methodologies (e.g. surveys on social media). Results show positive annual combined benefits of floodplain restoration measures, suggesting the helpfulness of evaluating these NBS through ES assessment. The work done will help increasing the knowledge on floodplain and their ES, and on how to rapidly evaluate them. Moreover, it will bring decision-makers further evidence in favor of floodplain restoration measures to be implemented for a general benefit of the communities.</p>

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