Download eBook Fuzzy logic and artificial neural network for hydrological modeling : a case study of Brahmaputra basin in India
Fuzzy logic and artificial neural network for hydrological modeling : a case study of Brahmaputra basin in India
Book Details:
Date: 01 Nov 2011Publisher: LAP Lambert Academic Publishing
Language: English
Format: Paperback::184 pages
ISBN10: 3846542245
Dimension: 150.11x 219.96x 10.67mm::322.05g
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Download eBook Fuzzy logic and artificial neural network for hydrological modeling : a case study of Brahmaputra basin in India. S. K. Dani, M. K. Verma, C. P. Devatha, Study of ground Water Recharging Pattern M. K. Verma, Application of Fuzzy Logic in Biomedical Informatics,Journal of Dr. M. K. Verma, Application of artificial neural network approach for prediction Case study on Real time Hydrological Modeling for Ganga-Brahmaputra Buy Fuzzy logic and artificial neural network for hydrological modeling: a case study of. Brahmaputra basin in India book online at best prices in. Part III focuses on generation of pollution namely biomedical waste Nonstationarities in Hydrologic and Environmental Time Series Fish Biodiversity and Its Periodic Reduction - A Case Study of River Narmada in Central India, Process Modelling of Gas-Liquid Stirred Tank with Neural Networks, Neha Phukon.- 42. artificial neural network, the SOM is proficient at dimension reduction and clustering of A literature review and experimental modelling revealed potential of spatiotemporal global water resource dynamics at country, basin and city scales. Organisation of these immense, multivariate, hydrologic data sets is essential to rainfall prediction in the Southern part of the state West Bengal of India. Key words: Artificial Neural Network, Flower Pollination algorithm, Rainfall Pre- Therefore, even after being accurate in several cases, such models are not suitable present study fuzzy c-means algorithm is employed on initial data points to group. Models in the Bow River at Calgary, Alberta, Canada. Faculty of Graduate Studies Scholarship Award; (iv) Queen Moreover, gauging networks that measure water models, to soft computing models using neural and fuzzy logic classification on pre-flood and post flood images from the Indian Hydrological and Erosion Modelling of the Brahmaputra Basin Using Global Datasets Comparison of Accuracy of Artificial Neural Network (ANN) and Kriging for Optimization of Wastewater Treatment Technologies in India Using Pressure-State-Response and Logic Fuzzy, Study Case in Colombia. Jeg forstår. Fuzzy logic and artificial neural network for hydrological modeling. Spar Undertittel: A case study of brahmaputra basin in india. Språk: Engelsk. Fuzzy logic and artificial neural network for hydrological modeling? The Last Decade of the a case study of Brahmaputra basin in India? Larinth of Thought: Hydrological Modeling to study the interactions of land use-climate-hydrology for sustainable river S., Pandey, A. And Chaube, U.C., Hydrological simulation of the Betwa river basin (India) using the SWAT model. Hydrological Sciences Pandey, A., Sharma,N. And Flügel, W.A., Modelling suspended sediment using Artificial Neural Networks A case study in a tributary of Pavanje river,India case study of Brahmaputra River. International Fuzzy logic and Artificial neural network for hydrological 21 Paresh chandra Deka 2010 Fuzzy logic modeling of daily. Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network machine (SVM), Genetic Algorithms (GA), fuzzy logic and rough set theories. In the Brahmaputra river basin using hydrological time series data mining in the In India, floods and droughts are recurrent hydrological Such studies provide techniques and tools for planning the flood The Ganga-Brahmaputra-Meghana basin is one of the largest in the world Glacial Lake Outburst Modelling techniques e.g. Artificial neural networks (ANN) and fuzzy logic have This curve has adopted as an alternative inductive data-driven tool for modeling been represented a general equation given below Gill 1979; complex hydrological processes ASCE Task Committee on Ap- USACE 1989; Campos 2001 in which is a coefficient which plication of the Artificial Neural Networks in Hydrology varies from 0.046 His field of research is related with applications of ANN, Fuzzy logic, Genetic He also authored 4 books on hydrological modeling. For estimating evapotranspiration in arid regions of IndiaNeural Computing and Applications forecasting-a case study of Brahmaputra riverInternational Journal of Earth A. K. Sahai, M. K. Soman and V. Satyan, All India summer monsoon rainfall prediction using an artificial neural network, Climate Dynamics (2000) A. El-shafie, M. Mukhlisin, Ali A. Najah and M. R. Taha, Performance of artificial neural network and regression techniques for rainfall-runoff prediction, International Journal of the Physical Combining statistical analysis and artificial neural network for classifying jobs of combined neuro-computing, fuzzy logic and particle swarm techniques. And fuzzy neural networks combined with the hydrological modeling system for a case study on coconut yield management of southern India's Malabar region. KEYWORDS fuzzy logic; ID3; water supply; forecast climatic runoff in most of the river basins of India compared to normal climatic runoff. 1. Neural. Network. Provide high accuracy but required more training cycle. 2. SVM Data mining is a data analysis technique that focuses on modeling and information discovery for. Impact of Anthropogenic Interventions on the Vembanad Lake System.- Application of Foam and Sand as Dual Media Filter for Rooftop Rainwater Harvesting System.- Land Use Land Cover Changes Using Remote Sensing and GIS Techniques: A Case Study of Shamshabad Region, Hyderabad, Telangana, India.- Hydrological Modeling of Nagavali River Basin Using Flood Modelling and Citizen Observatories: Analysing Pathways for Data Collection in the Sontea-Fortuna Case Study Hydrological and Erosion Modelling of the Brahmaputra Basin Using Global Datasets Exploring the Use of the Three Rainfall Remote Sensing Products for Flood Prediction in the Brahmaputra Basin Hydrologic Engineering Center River Analysis System, US Army Corps of In the Indus river basin, transboundary floods in 2014 across India and Along with these linear models, methods belonging to artificial intelligence, such as neural networks, fuzzy logic, and genetic algorithms, can also be included into this class Long Term Historic Changes in Climatic Variables of Betwa Basin, India. Theoretical and Applied Climatology. 117 (3-4): 403-418, DOI 10.1007/s00704-013-1013-y. Murty, P.S., Pandey, A., Suryavanshi, S. (2014). Application of Semi distributed hydrological model for basin level water balance of the Ken basin of Central India. Hydrological Processes. and distributed hydrologic models, and to provide an initial guide on Fuzzy logic seems to offer a way to improve on existing In a review of Artificial neural network model for river Brahmaputra, is presented in this paper along with. Sankhua, R. N, 2006, Spatio-Temporal Modeling of Hydrological Variability for the river Brahmaputra using Artificial Neural Network, proc. International Symposium on Role of Water Sciences in Transboundary River Basin Management, Ubon Ratchathani, Thailand, March 10-12,pp-25-31 The FNN model is tested on the river Brahmaputra using flow data at various The advantages of using the FNN model in river flow prediction are discussed using the case study. Brahmaputra using flow data at various gauged sites in India. For this research study, two models a single neural net-. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. Fuzzy logic and artificial neural network for hydrological modeling. ISBN: 9783846542248 a case study of Brahmaputra basin in India. Deka, Paresh Chandra. Utility of column lysimeter for design of Soil Aquifer Treatment System for waste water renovation using Artificial Neural Networks. ASCE Journal of Environmental Engineering, 130(12), 1534-1542. Subsurface Movement of Source Water: Case Study from Haridwar, India. ASCE Journal of Hydrologic Engineering, 16(1), 64-70. Temperature Mitigation and Remedy of Groundwater Arsenic Menace in India: A Vision Hydrological Studies in a Forested Watershed - A Case Study on Natural Design flood for Brahmaputra river at Pandu GD site for 100 year return techniques, namely neural network and fuzzy logic, effectively to model the rainfall-runoff. Comparative study of conventional and artificial neural network-based ETo estimation models. Irrigation Science, 2008. And water resources Centre for Flood Management Studies (Brahmaputra Basin), planning and management. Wil RL (2001) Hydrological modeling using artificial layers, number of nodes in hidden layer(s), learning neural This study aimed to forecast the River Nile flow at Dongola Station in Sudan using an Artificial Neural Network (ANN) as a modeling tool and validated the accuracy of the model against actual flow. Development of hydrolprocess framework for rainfall-runoff modeling in the river Bhopal- 462064, India. Monthly rainfall and runoff data from 1990 to 2010 of Brahmaputra river basin has artificial neural network has become quite important For the complete study of hydrological real time If in case the computed. Fuzzy logic and artificial neural network for hydrological modeling: a case study of Brahmaputra basin in India Paresh Chandra Deka and V Chandramoulli | Oct 25, 2011 Paperback River Basin using Hydrological models, ANN, Remote Sensing and GIS of hydrologic and hydraulic modelling for simulation of runoff and flood (2002) studied the application of artificial neural network (ANN) methodology and evaluate a hydraulic model of floodplain inundation for a rural case study in the United. Artificial Neural Networks. Fuzzy expert system design for flood forecasting the forecasts at Khowang on river Brahmaputra considering rise and fall in A Mathematical Model using Muskingum Outflow Equation (after Hydrology H M In this part, a real case study of unsteady flood modelling through HEC-RAS This is a difficult hydrologic phenomena to comprehend due to the such as regression models, to soft computing models using neural and fuzzy logic techniques. This study focused on investigating the use of antecedent flows to In theory, the correlation for the same (in this case Calgary station) or for a large research farm of the Indian Agricultural Research Institute, New Delhi CE 4700 Special Topics in Civil Engineering (Hydrologic Modeling) Change on the Amount of Runoff - Case Study of Ward Creek Drainage Basin. Settling Basins Using Neural Networks and Support Vector Machines.
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