CEST Proceedings are published under the ISSN 2944-9820.
Statistical prediction models for the odour emissions quantification in terms of odour concentration: Analysis and comparison
(Corresponding) Galang M., Ballesteros Jr. F., Zarra T., Naddeo V., Belgiorno V.
Topic:
Environmental odour, monitoring and control
Measuring odour concentration is a significant step to achieve efficient environmental odour management in continuous, objective and repeatable manner. To deal with this, researchers developed instrumental odour monitoring systems (IOMS) by applying odour monitoring models (OMM) for prediction. At...Read more
Prediction of Algal Bloom Occurrence in Laguna Lake, Philippines using Artificial Neural Networks (ANN)
(Corresponding) Esguerra G., Ballesteros F.
Topic:
Lakes, rivers, estuaries and ecosystem health
Algal blooms pertain to an undesirable formation of unicellular freely-floating algal scum caused by the rapid growth of phytoplankton, which can become a hazard for the water body ecosystem. Laguna Lake serves as both a source of livelihood and water supply for the residents in the region and the...Read more
Keywords:
algal blooms, phytoplankton counts, artificial neural networks, Laguna de Bay, water quality prediction
Artificial Neural Network (ANNs) for predicting petroleum hydrocarbons from heavy metals contaminated soils around fuel stations
(Corresponding) Bonelli M., Manni A., Saviano G.
Topic:
Environmental data analysis and modelling
Petrol stations are classified as a dangerous source of pollution for the human population due to the toxicity of emissions from evaporated vehicle fuels and fuel spillages. The contaminants released in the environment are mainly complex mixtures of petroleum hydrocarbon compounds (PHCs) and heavy...Read more
Keywords:
Artificial Neural Network predictions, heavy metals, field portable XRF, petroleum hydrocarbon compounds
Modelling the operation of a Water Treatment Plant based on Artificial Neural Networks
(Corresponding) GYPARAKIS S., TRICHAKIS I., VAROUCHAKIS E., DIAMADOPOULOS E.
Topic:
Water treatment
The main purpose of this study is to model the operation of a Drinking Water Treatment Plant (DWTP) using its main operational and water quality parameters in a fast, easy and reliable way. This study is based on a large number of data from recent years (2019-2021). The DWTP has a maximum capacity...Read more
Comparison of regression model and artificial neural network model in noise prediction in a mixed area of Dhaka City
(Corresponding) Chowdhury V., Zarif S., Tofa T., Laskar M.
Topic:
Environmental data analysis and modelling
The equivalent noise levels regularly exceed acceptable limits within Dhaka city, the capital of Bangladesh, especially in the mixed urban areas (where trips are generated to serve commercial, residential, and industrial demands). The study aims to assess the noise level in mixed urban areas, build...Read more
Nonlinear Autoregressive Neural Networks for Air Temperature forecasting
Philippopoulos K., (Corresponding) Tzanis C., Deligiorgi D., Alimissis A.
Topic:
Environmental data analysis and modelling
In the field of climatic conditions forecasting, the linear classical time series models are inadequate for modelling and predicting accurately the air temperature variability. This work presents the novel application of nonlinear autoregressive neural networks (NAR) in air temperature forecasting...Read more
A versatile decision-support tool to assess air quality and health effects
(Corresponding) Relvas H., Ferreira J., Lopes D., Rafael S., Almeida S., Diapouli E., Miranda A.
Topic:
Air pollution
This work presents a web-based policy tool for the development of effective particulate matter (PM) pollution strategies. The tool is based on an integrated modelling approach, from emissions to health effects, which allows testing measures to improve air quality, focused on PM2.5 levels, and...Read more
Sustainable Smart Agriculture: Plant disease detection with deep learning techniques in cotton cultivation
(Corresponding) Kounani A., Lavazos N., Tsimpiris A., Varsamis D.
Topic:
ARTIFICIAL INTELLIGENCE IN ENVIRONMENTAL APPLICATIONS
To meet the needs of an ever-growing global population, the agricultural sector has the responsibility of increasing production, managing diseases and pests that attack crops, and implementing sustainable practices. Deep learning techniques used in sustainable smart agriculture have proven...Read more
A Deep Learning Model, interpreted with an XAI technique, to simulate and optimize the remediation of oil-drilling cuttings in bubble flow reactors
Kalari K., Christodoulis K., Bali N., Theodoropoulou M., (Corresponding) Tsakiroglou C.
Topic:
Environmental data analysis and modelling
A multitask deep neural network (DNN) is developed to simulate the ozonation of oil-drilling cuttings (ODC) and is interpreted through a technique of explainable artificial intelligence (XAI) to provide knowledge about the experimental conditions that will maximize the decontamination of ODC. On a...Read more
Artificial Neural Network prediction of citizens' climate mitigation perception related to urban green infrastructure: The Case of Drama City in Greece:
(Corresponding) KAZANA V., RAPTIS D., CHRYSANTHIDOU E., KAZAKLIS A., MOUMTZIDOU D., PAPADOPOULOU D.
Topic:
CITIZEN SCIENCE
Social perceptions and attitudes are important dimensions for the successful implementation of Green Infrastructure (GI) projects, as these are typically designed to improve environmental conditions and promote a green economy. This paper attempts to investigate social behavioral patterns of...Read more
Keywords:
Human Thermal Comfort, Artificial Neural Networks, urban green infrastructure, public perception, climate mitigation
