3,627 research outputs found
HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS
Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing
Geo-physical parameter forecasting on imagery{based data sets using machine learning techniques
>Magister Scientiae - MScThis research objectively investigates the e ectiveness of machine learning (ML) tools
towards predicting several geo-physical parameters. This is based on a large number
of studies that have reported high levels of prediction success using ML in the eld.
Therefore, several widely used ML tools coupled with a number of di erent feature sets
are used to predict six geophysical parameters namely rainfall, groundwater, evapora-
tion, humidity, temperature, and wind. The results of the research indicate that: a)
a large number of related studies in the eld are prone to speci c pitfalls that lead to
over-estimated results in favour of ML tools; b) the use of gaussian mixture models as
global features can provide a higher accuracy compared to other local feature sets; c)
ML never outperform simple statistically-based estimators on highly-seasonal parame-
ters, and providing error bars is key to objectively evaluating the relative performance
of the ML tools used; and d) ML tools can be e ective for parameters that are slow-
changing such as groundwater
Global detection and analysis of coastline associated rainfall using an objective pattern recognition technique
Coastally associated rainfall is a common feature especially in tropical and
subtropical regions. However, it has been difficult to quantify the
contribution of coastal rainfall features to the overall local rainfall. We
develop a novel technique to objectively identify precipitation associated with
land-sea interaction and apply it to satellite based rainfall estimates. The
Maritime Continent, the Bight of Panama, Madagascar and the Mediterranean are
found to be regions where land-sea interactions plays a crucial role in the
formation of precipitation. In these regions 40% to 60% of the total
rainfall can be related to coastline effects. Due to its importance for the
climate system, the Maritime Continent is a particular region of interest with
high overall amounts of rainfall and large fractions resulting from land-sea
interactions throughout the year. To demonstrate the utility of our
identification method we investigate the influence of several modes of
variability, such as the Madden-Julian-Oscillation and the El Ni\~no Southern
Oscillation, on coastal rainfall behavior. The results suggest that during
large scale suppressed convective conditions coastal effects tend modulate the
rainfall over the Maritime Continent leading to enhanced rainfall over land
regions compared to the surrounding oceans. We propose that the novel objective
dataset of coastally influenced precipitation can be used in a variety of ways,
such as to inform cumulus parametrization or as an additional tool for
evaluating the simulation of coastal precipitation within weather and climate
models
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
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