38 research outputs found
Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir
The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines
the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since
they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have
increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts
in the last decades has been the construction of hydroelectric power plants.
As a result, dramatic altering of these ecosystems has been observed, including changes in
water levels, decreased oxygenation and loss of downstream organic matter, with consequent
intense land use and population influxes after the filling and operation of these reservoirs. This,
in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation.
The fishing industry in place before construction of dams and reservoirs, for example, has become
much more intense, attracting large populations in search of work, employment and income.
Environmental monitoring is fundamental for reservoir management, and several studies
around the world have been performed in order to evaluate the water quality of these ecosystems.
The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which
are very importante since their study aids in monitoring anthropogenic environmental impacts
and can lead to policy and decision making with regard to environmental management of this
area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological
cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics.
Eutrophication, one of the main processes leading to water deterioration in lentic environments,
is mostly caused by anthropogenic activities, such as the releases of industrial and domestic
effluents into water bodies.
Physico-chemical water parameters typically related to eutrophication are, among others,
chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess
the eutrophic state of water bodies.
Usually, these parameters must be investigated by going out to the field and manually
measuring water transparency with the use of a Secchi disk, and taking water samples to the
laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These
processes are time- consuming and require trained personnel. However, we have proposed other
techniques to environmental monitoring studies which do not require fieldwork, such as remote
sensing and computational intelligence.
Simulations in different reservoirs were performed to determine a relationship between these
physico-chemical parameters and the spectral response. Based on the in situ measurements,
empirical models were established to relate the reflectance of the reservoir measured by the
satellites. The images were calibrated and corrected atmospherically.
Statistical analysis using error estimation was used to evaluate the most accurate methodology.
The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical
parameters of the water from the reflectance of visible bands and NIR of satellite images,
with better results for the period with few clouds in the regions analyzed.
The present study shows the application of wavelet neural network to estimate water quality
parameters using concentration of the water samples collected in the Amazon reservoir and Cefni
reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by
hydrological cycle.
The trained ANNs demonstrated good results between observed and estimated after Atmospheric
corrections in satellites images. The ANNs showed in the results are useful to estimate
these concentrations using remote sensing and wavelet transform for image processing.
Therefore, the techniques proposed and applied in the present study are noteworthy since
they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management
and policy decision-making processes.
The tests results showed that the predicted values have good accurate. Improving efficiency
to monitor water quality parameters and confirm the reliability and accuracy of the approaches
proposed for monitoring water reservoirs.
This thesis contributes to the evaluation of the accuracy of different methods in the estimation
of physical-chemical parameters, from satellite images and artificial neural networks. For future
work, the accuracy of the results can be improved by adding more satellite images and testing
new neural networks with applications in new water reservoirs
EG-ICE 2021 Workshop on Intelligent Computing in Engineering
The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways