918 research outputs found
Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis
In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers
A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Qualityâs Parameters: Current Trends and Future Directions
Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables
A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs
Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxinâs effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems
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
Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan
Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management
Sustainable marine ecosystems: deep learning for water quality assessment and forecasting
An appropriate management of the available resources within oceans and coastal regions is
vital to guarantee their sustainable development and preservation, where water quality is a key element.
Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet
of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim.
In this paper, we review methodologies and technologies for water quality assessment that contribute to a
sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for
water quality estimation and forecasting. The analyzed literature is classified depending on the type of task,
scenario and architecture. Moreover, several applications including coastal management and aquaculture
are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where
transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies
are expected to be the main involved agents.Postprint (published version
Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective
The communityâs well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the modelsâ concepts and historical uses would be beneficial in preventing researchers from overlooking modelsâ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the âstate of the artâ on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed
Water Quality Prediction Based on Machine Learning Techniques
Water is one of the most important natural resources for all living organisms on earth. The monitoring of treated wastewater discharge quality is vitally important for the stability and protection of the ecosystem. Collecting and analyzing water samples in the laboratory consumes much time and resources. In the last decade, many machine learning techniques, like multivariate linear regression (MLR) and artificial neural network (ANN) model, have been proposed to address the problem. However, simple linear regression analysis cannot accurately forecast water quality because of complicated linear and nonlinear relationships in the water quality dataset. The ANN model also has shortcomings though it can accurately predict water quality in some scenarios. For example, ANN models are unable to formulate the non-linear relationship hidden in the dataset when the input parameters are ambiguous, which is common in water quality dataset.
The adaptive neuro-fuzzy inference system (ANFIS) has been proven to be an effective tool in formulating the complicated linear and non-linear relationship hidden in datasets. Although the ANFIS model can achieve good performance in the water quality prediction, it has some limitations. Firstly, the size of the training dataset should not be less than the number of training parameters required in the model. Secondly, when the data distribution in the testing dataset is not reflected in the training dataset, the ANFIS model may generate out-of-range errors. Lastly, a strong correlation is required between input and target parameters. If the correlation is weak, the ANFIS model cannot accurately formulate the hidden relationship.
In this dissertation, several methods have been proposed to improve the performance of ANFIS-based water quality prediction models. Stratified sampling is employed to cover different kinds of data distribution in the training and testing datasets. The wavelet denoising technique is iv used to remove the noise hidden in the dataset. A deep prediction performance comparison between MLR, ANN, and ANFIS model is presented after stratified sampling and wavelet denoising techniques are applied. Because water quality data can be thought as a time series dataset, a time series analysis method is integrated with the ANFIS model to improve prediction performance. Lastly, intelligence algorithms are used to optimize the parameters of membership functions in the ANFIS model to promote the prediction accuracy. Experiments based on water quality datasets collected from Las Vegas Wash since 2007 and Boulder Basin of Lake Mead, Nevada, between 2011 and 2016 are used to evaluate the proposed models
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