Incorporating stakeholders’ preferences into a multi-criteria framework for planning large-scale Nature-Based Solutions

Hydro-meteorological risks are a growing issue for societies, economies and environments around the world. An effective, sustainable response to such risks and their future uncertainty requires a paradigm shift in our research and practical efforts. In this respect, Nature-Based Solutions (NBSs) offer the potential to achieve a more effective and flexible response to hydro-meteorological risks while also enhancing human well-being and biodiversity. The present paper describes a new methodology that incorporates stakeholders’ preferences into a multi-criteria analysis framework, as part of a tool for selecting risk mitigation measures. The methodology has been applied to Tamnava river basin in Serbia and Nangang river basin in Taiwan within the EC-funded RECONECT project. The results highlight the importance of involving stakeholders in the early stages of projects in order to achieve successful implementation of NBSs. The methodology can assist decision-makers in formulating desirable benefits and co-benefits and can enable a systematic and transparent NBSs planning process.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01419-4) contains supplementary material, which is available to authorized users.     

Neural network modeling of dissolved oxygen in the Gru?a reservoir, Serbia

The objective of this study is to develop a feedforward neural network (FNN) model to predict the dissolved oxygen in the Gru?a Reservoir, Serbia. The neural network model was developed using experimental data which are collected during a three years. The input variables of the neural network are: water pH, water temperature, chloride, total phosphate, nitrites, nitrates, ammonia, iron, manganese and electrical conductivity. Sensitivity analysis is used to determine the influence of input variables on the dependent variable. The most effective inputs are determined as pH and temperature, while nitrates, chloride and total phosphate are found to be least effective parameters. The Levenberg-Marquardt algorithm is used to train the FNN. The optimal FNN architecture was determined. The FNN architecture having 15 hidden neurons gives the best choice. Results of FNN models have been compared with the measured data on the basis of correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). Comparing the modelled values by FNN with the experimental data indicates that neural network model provides accurate results.     

Particle formation due to brake wear,influence on the people health and measures for their reduction: a review

Environmental Science and Pollution Research - For achieving the desired vehicle speed, the IC engine is very important, while for further vehicle speed maintaining and adaptation to road...