neural network modeling Articles

  • A flood forecasting neural network model with genetic algorithm

    It will be useful to attain a quick and accurate flood forecasting, particularly in a flood-prone region. The accomplishment of this objective can have far reaching significance by extending the lead time for issuing disaster warnings and furnishing ample time for citizens in vulnerable areas to take appropriate action, such as evacuation. In this paper, a novel hybrid model based on recent ...


    By Inderscience Publishers

  • Prediction of meteorological variables using artificial neural networks

    In the present study, artificial neural network (ANN) models are developed to predict seven meteorological parameters such as maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours, dew point and evaporation individually. To predict each of the meteorological parameters, recurrent MISO-ANN network is used. Totally seven ANN models are developed and the development ...


    By Inderscience Publishers

  • Studies of air quality predictors based on neural networks

    In recent years, urban air pollution has emerged as an acute problem because of its negative effect on health and living conditions. Regional air quality problems, in general, are linked to violations of specified air quality standards. The current study aims to find neural network based air quality predictors, which can work with a limited number of datasets and are robust enough to handle data ...


    By Inderscience Publishers

  • A neural-network-based variance decomposition sensitivity analysis

    This paper describes the implementation of an artificial neural network for obtaining the numerous model output calculations within a variance decomposition scheme for performing the model sensitivity analysis with respect to both individual and grouped parameters. A case study concerning the identification of the input variables mostly contributing to model output uncertainty, with reference to ...


    By Inderscience Publishers

  • The application of artificial neural networks for the optimization of coagulant dosage

    Filtration is the final physical barrier preventing the passage of microbial pathogens into public drinking water. Proper pre-treatment via coagulation is essential for maintaining good particle removal during filtration. To improve filter performance at the Elgin Area WTP, artificial neural network (ANN) models were applied to optimize pre-filtration processes in terms of settled water turbidity ...


    By IWA Publishing

  • Application of neural network to wind energy conversion systems

    In the previous study, the energy generation of two WECS deployed at Darling site were estimated using the statistical–based technique. A total energy generation of 178.43 MWh at 50 m and 196.17 MWh at 70 m height were estimated from both WECS for the month of January 2010. In this study, the energy generations of both WECS for the same month were estimated using a developed wind power predictor. ...


    By Inderscience Publishers

  • Coupling of neural network and dispersion models: a novel methodology for air pollution models

    Supervised neural net models and dispersion models are two important approaches for evaluating air pollution concentrations. The authors propose the development of an integrated model, in order to optimise the performances of each methodology. The concentrations evaluated by an air pollution model are coupled with a Neural Net (NN), so as to adjust the influence of important variables on ...


    By Inderscience Publishers

  • Prediction of lake eutrophication using artificial neural networks

    An artificial neural network (ANN), which is a data–driven modelling approach, is proposed to indicate the water quality of Lake Fuxian, the deepest lake of southwest China. To determine the nonlinear relationships between the water quality factors and eutrophication indicators, several ANN models were chosen. The back–propagation and radial basis function neural network models were applied to ...


    By Inderscience Publishers

  • Neural network model for identification of societal preference of environmental issues

    A new method for identification of preferences of environmental issues using the societal approach is suggested. The preferences assigned by different economic groups to 11 environmental issues are obtained through analysis of linguistically stated relative rankings using fuzzy partial ordering method. The system identification technique based on neural networks is used to identify a logical ...


    By Inderscience Publishers

  • Modelling resorcinol adsorption in water environment using artificial neural network

    The application of Artificial Neural Network (ANN) for the prediction of removal efficiency of resorcinol in water environment using low-cost carbonaceous adsorbents such as rice husk ash was studied in the present investigation. The input data used for training of the ANN model include adsorbent dose, adsorbate concentration, time of contact and pH. The various input variables were obtained in a ...


    By Inderscience Publishers

  • Predicting air quality in Uberlandia, Brazil, using linear models and Neural Networks

    Particulate air pollution causes a wide range of effects on human health, including disorders of the respiratory and cardiovascular systems, asthma and can cause mortality. Hence, the development of an efficient air quality forecasting and early warning system is an obvious and imperative need. The objective of this work was to investigate this forecasting possibility using linear models (such as ...


    By Inderscience Publishers

  • Modelling urban air quality using artificial neural network

    This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been ...


    By Springer

  • Neural network and genetic programming for modelling coastal algal blooms

    In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they ...


    By Inderscience Publishers

  • Artificial neural network ensemble modeling with exploratory factor analysis for streamflow forecasting

    An artificial neural network (ANN) is a powerful data-driven modeling tool. The selection of the input variable is an important task in the development of an ANN model. However, at present in ANN modeling, the input variables are usually determined by trial and error methods. Further, the ANN modeler usually selects a single ‘good’ result, and accepts it as the final result without ...


    By IWA Publishing

  • Alternate neural network models in decision making for socio-economic development

    Alternate neural network models are used to identify the structure of preferences for development alternatives and their consequences in the cases of Doon Valley and National Capital Region in India in the context of carrying-capacity-based developmental planning. Alternate neural network models are presented as an effective alternative to deal with multi-criteria decision-making situations. The ...


    By Inderscience Publishers

  • Fuzzy Logic and Artificial neural network approaches for dissolved oxygen prediction

    A study on application of data-driven models namely the rule-based model based on mamdani Fuzzy Logic and Artificial Neural Network model in predicting dissolved oxygen in an effluent-impacted urban river is presented and compared. Combined rule bases were formed from the generated fuzzy rules for input – output mapping. Predictability of both the models was good with better performance for ...


    By Inderscience Publishers

  • Predicting the performance of multi-media filters using artificial neural networks

    The impact of flow rate and turbidity on the performance of multi-media filtration has been studied using an artificial neural network (ANN) based model. The ANN model was developed and tested based on experimental data collected from a pilot scale multi-media filter system. Several ANN models were tested, and the best results with the lowest errors were achieved with two hidden layers and ...


    By IWA Publishing

  • Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model

    Four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment. Different algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance. The results also highlight issues in selecting analytical methods to compare outputs ...


    By IWA Publishing

  • Development of a discharge equation for side weirs using artificial neural networks

    ABSTRACT Flow over a side weir is one of the more complex flows to simulate in one-dimensional unsteady flow analysis. Various experiments have been applied, but no agreement is apparent in the literature about the best method. In this study, an Artificial Neural Network model has been used to extract a discharge equation for side weirs which accurately estimates overflow discharges. The ...


    By IWA Publishing

  • Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach

    Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a ...


    By Springer

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