Modeling Discharge And Water Chemistry Using Artificial Neural Network
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| Author | : Toluwaleke Ajayi |
| Publisher | : |
| Total Pages | : 255 |
| Release | : 2021 |
| Genre | : Acid mine drainage |
| ISBN | : |
In southeast Ohio, Raccoon Creek Watershed (RC) has an extensive mining history resulting in acid mine drainage (AMD) and subsequent environmental problems. Modeling of the discharge and chemistry, as well as an assessment of the impact of climate change in discharge and chemistry of AMD, impacted Hewett Fork, a tributary of Raccoon Creek, is the focus of this thesis. Discharge measurements are collected by the United States Geological Survey (USGS) gage station at the Bolin Mills (BM) station on the main stem of Raccoon Creek. This data for the period 2011-2019 has been analyzed to develop a prediction model for BM discharge, in addition to assessing the impact of climate change on BM flow event under two climate scenario (RCP4.5 and RCP 8.5) and subsequently use the model to predict flow and water chemistry in Hewett Fork. Precipitation, antecedent precipitation index (API), and air temperature were the input variables considered in this study for transient data analysis using the program PAST, and additional variable such as antecedent temperature index (ATI), and potential evapotranspiration (ET) for modeling studies using Artificial Neural Networks (program NeuroShell2). The result of the transient data analysis highlighted the quick flow, baseflow of the watershed, delay time, and total response time. The immediate response time (zero-day lag) of BM discharge following API, in addition to its strong spectrum signal (0.22Hz), indicates a strong influence of API on BM discharge. The Neural Network Model using group method of data handling (GMDH) and generalized regression neural network (GRNN) shows a variation of prediction models for BM due to parameters such as decay factor in API and ATI, as well as in the evapotranspiration input variables of the model. However, the study reveals that the GRNN model for BM is the most suitable for BM prediction based on its performance, with an r-value greater than 0.90, and its ease in predicting discharge by specifying a data set to be added to the data set for training and calibrating the network. The result of the water chemistry model using GMDH, with r values greater than 0.80 for each model, shows the input variables have a good capacity to predict chemical concentration/load in the HF stream. Five climate model projections for the future periods 2020-2039, 2040-2059, 2060-2079, and 2080-2099 were analyzed in this study to simulate three flow events(high, low, and intermediate flow) in BM. The flows simulated by the GRNN model in response to the future climate model projections showed a consistent increase in low flow and high flow and a decrease in intermediate flow for all future time intervals.t
| Author | : R.S. Govindaraju |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 338 |
| Release | : 2013-03-09 |
| Genre | : Science |
| ISBN | : 9401593418 |
R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.
| Author | : Robert Abrahart |
| Publisher | : CRC Press |
| Total Pages | : 316 |
| Release | : 2004-05-15 |
| Genre | : Science |
| ISBN | : 0203024117 |
A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography. Each chapter has been written by one or more eminent experts working in various fields of hydrological modelling. The b
| Author | : Srinivasa Lingireddy |
| Publisher | : ASCE Publications |
| Total Pages | : 196 |
| Release | : 2005-01-01 |
| Genre | : Technology & Engineering |
| ISBN | : 9780784475607 |
Prepared by the Water Supply Engineering Technical Committee of the Infrastructure Council of the Environmental and Water Resources Institute of ASCE. This report examines the application of artificial neural network (ANN) technology to water supply engineering problems. Although ANN has rarely been used in in this area, those who have done so report findings that were beyond the capability of traditional statistical and mathematical modeling tools. This report describes the availability of diverse applications, along with the basics of neural network modeling, and summarizes the experiences of groups of researchers around the world who successfully demonstrated significant benefits from using ANN technology in water supply engineering. Topics include: Forecasting salinity levels in River Murray, South Australia; Predicting gastroenteritis rates and waterborne outbreaks; Modeling pH levels in a eutrophic Middle Loire River, France; and ANNs as function approximation tools replacing rigorous mathematical simulation models for analyzing water distribution networks.
| Author | : Catherine Murray |
| Publisher | : |
| Total Pages | : 346 |
| Release | : 2003 |
| Genre | : Water quality |
| ISBN | : |
| Author | : Pijush Samui |
| Publisher | : Elsevier |
| Total Pages | : 592 |
| Release | : 2021-02-05 |
| Genre | : Technology & Engineering |
| ISBN | : 0128208775 |
Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering. This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering. Includes firsthand experience about artificial intelligence models, utilizing case studies Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow Presents the application of new instruments in water engineering
| Author | : Ayman S. El-Baz |
| Publisher | : Academic Press |
| Total Pages | : 326 |
| Release | : 2021-07-21 |
| Genre | : Science |
| ISBN | : 0128218495 |
State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more. Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as detection, segmentation, and classification via AI
| Author | : Alaa El Din Mahmoud |
| Publisher | : CRC Press |
| Total Pages | : 311 |
| Release | : 2023-04-25 |
| Genre | : Technology & Engineering |
| ISBN | : 100082974X |
Artificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures. FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.
| Author | : Management Association, Information Resources |
| Publisher | : IGI Global |
| Total Pages | : 1575 |
| Release | : 2021-07-16 |
| Genre | : Computers |
| ISBN | : 1668424096 |
Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.
| Author | : Robert James May |
| Publisher | : |
| Total Pages | : 341 |
| Release | : 2009 |
| Genre | : Neural networks (Computer science) |
| ISBN | : |
Modelling water quality within complex, man-made and natural environmental systems can represent a challenge to practitioners. Many conventional modelling tools are not capable of representing the complexities of physical and chemical processes often observed in these systems. Consequently, there has been a great deal of interest in the application of computational intelligence techniques, such as artificial neural networks (ANNs). However, "black-box" approaches, such as ANN modelling, are often criticised due to a perceived lack of transparency in the model development methodology. This research has therefore focussed on improving the tools and techniques that are used in the development of ANN models for water quality prediction and forecasting.
