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Dandagala, Sreenivasulu
- Evaluation of GRNN and RBF Model Performance for Groundwater Level Forecasting at Southwest Coast of India
Authors
1 Department of Civil Engineering, Sree Vidyanikethan Engineering College, Sree Sainath Nagar, Tirupati, A. Rangampet-517102, Andhra Pradesh, IN
2 Department of Civil Engineering, Bearys Institute of Technology, Innoli, Boliyar Village, Near Mangalore University, Mangalore – 574153, Karnataka, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 8 (2013), Pagination: 359-365Abstract
An accurate and reliable forecast plays a vital role for proper planning and utilization of groundwater resources in a sustainable manner. In the present work, an investigation has been carried out in selective wells based on different land use/land cover in the micro watershed located southwest coast of India. The present study utilizes the Generalized Regression Neural Network (GRNN) for forecasting groundwater level (GWL) and compares its performance with that of the Feed Forward Back Propagation (FFBP) trained with Levenberg Marquartz (LM)] and Radial Basis Function (RBF). Weekly time series groundwater level data were used for span of three years (2004-2007). The comparative analysis of the obtained results showed that the GRNN and RBF have the superiority over the FFBP methods for forecasting groundwater level. On the basis of performance criteria (i.e lower RMSE and higher CE), GRNN yielded the better performance to RBF considering the models developed in the study.
Keywords
ANN, FFBP, GRNN, GWL, RBF.
- Neural Network for Ocean Wave Forecasting
Authors
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology, Karnataka, Surathkal, 575025, IN
2 Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, 575025, IN
3 Center for Water Resources, Anna University, Chennai-600025, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 3 (2012), Pagination: 167-170Abstract
Forecasting of wave parameters is necessary for many
marine and coastal operational related activities. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the waveheight for the next 3hr, 6hr, 9hr, 12hr, 24hr, 48hr, 72hr, 96hr and 120hr in the Mangalore region, southwest coast of India. For this purpose two different models namely, Feed Forward Back Propagation (FFBP) and Nonlinear Auto Regressive Model with eXogenous input (NARX) of the ANN were used. The performances of developed models were evaluated using performance indices such as RMSE and CE. The CE values in FFBP model ranged from 0.997 to 0.785 while in NRAX model CE values are between 0.995 and 0.806 for the prediction time from 3hr to 120hr. A better agreement was observed between the observed and predicted waves for NRAX than that of FFBP for smaller (3-12hr) and larger lead period (24-120hr). Thus the NARX model performs better than the FFBP in terms of prediction capability and accuracy.
Keywords
Waveheight, Prediction, ANN, FFBP, NRAX, RMSE.- Investigation of the Effects of Meteorological Parameters on Groundwater Level using ANN
Authors
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal-575025, Karnataka (D.K), IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 1 (2012), Pagination: 39-44Abstract
In the present research the effect of meteorological parameters such as temperature, relative humidity, evaporation and rainfall on groundwater level fluctuation has been investigated for Dakshina Kannada coastal aquifer at southwest coast of India. Weekly time series meteorological data were used for a span of three years (2004-2007). Generalized regression neural network (GRNN) and feed-forward back propagation networks (FFBP) were employed to develop various models. Model Input combinations were selected based on autocorrelation. The performances of developed models were evaluated using performance indices such as ischolar_main mean square error (RMSE) and coefficient of efficiency (CE). The obtained results showed closed relationship between rainfall event and groundwater level during monsoon. It was also, observed that the temperature and evaporation had significant effect on groundwater level fluctuations in non-monsoon season. The obtained GRNN results were compared with that of FFBP. A better agreement was observed between the actual and modeled groundwater levels for GRNN than that of FFBP. From the study, GRNN can be applied successfully for forecasting groundwater level due to its accuracy and reliable results.
Keywords
Artificial Neural Network, Generalized Regression Neural Network, Groundwater level, Feedforward Back Propagation.- Artificial Neural Networks Applications in Groundwater Hydrology-A Review
Authors
1 Department of Civil Engineering, Sree Vidyanikethan Engineering College, Sri Sainath Nagar, Rangampet-517102, IN
2 Department of Civil Engineering, Bearys Institute of Technology Mangalore, Innoli, Boliyar Village, Near Mangalore University, Mangalore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 9 (2017), Pagination: 182-187Abstract
Reliable groundwater level forecasting is crucial and has become challenging task for the groundwater hydrologists. An accurate forecast plays a vital role for proper planning and utilization of groundwater resources in a sustainable manner. In the present study an attempt has been made to study one of the soft computing techniques such as Artificial Neural Networks (ANN) and its various applications in groundwater hydrology. Accurate prediction of Groundwater Level (GWL), assessment of water quality, concentrations of contamination, estimation of various aquifer properties and dynamic groundwater levels are the complex problems in the domain of groundwater hydrology. The ANN applications are studied in detailed manner and discussed its merits and demerits and given a brief discussion on its scope for future research work in the groundwater hydrology. Moreover, type of time series data, quality of data and other aspects would limit the applications of the modeling and requires further any model development. In the time series analysis, ANN has wide applications in the domain of civil engineering due to its capability of non-linear modelling in real world complex phenomena. ANN is non parametric method and prior knowledge is not mandatory. Above all these features makes ANN more attractive for time series modelling and forecasting. However, in the past a lot of successful applications have shown that ANN provide powerful tool for time series modeling. Therefore, based on the literature cited on ANN applications in the domain of groundwater hydrology, ANN can be suggested to be one of the effective tool for better GWL forecasting even with limited data.Keywords
Groundwater Level, Artificial Neural Networks, Time Series.- Applications of Artificial Neural Network for Streamflow Forecasting-A Review
Authors
1 Department of Civil Engineering, G. Pulla Reddy Engineering College Kurnool, IN
2 Department of Civil Engineering S.V.U. College of Engineering Tirupati – 517 502 Andhra Pradesh, IN
3 Department of Civil Engineering, Sree Vidyanikethan Engineering College, A. Rangampet Tirupati – 517 102 Andhra Pradesh, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 2 (2018), Pagination: 25-29Abstract
Estimating streamflow is important in determining the water resource availability and assessing the flood, drought management and mitigation studies. Continuous investigation of streamflow history and monitoring of streamflow data is an effective way to establish a reliable forecast. These forecasting requires long length of data to analysis. The analysis can be done based on traditional methods. These methods require, more number of data, time consuming and tedious process. Therefore, these forecasts can hamper the development and management of water managers or authorities to effective utilization of water resources in a suitable manner. Therefore, there is a need of the hour to search alternative methods for the reliable forecasts. Data driven models such as Artificial Neural Networks (ANN) have proven to be an efficient alternative to traditional methods for assessing and modeling quantitative and qualitative in the domain of water resources engineering and management. Therefore, in the present paper an attempt have been made to investigate to study the applications of ANN in streamflow forecasting. Selected ANNs applications are only reviewed in the current paper. Soft computing tools are becoming popular in solving hydrological problems. Among the various soft computing methods ANN tools have immense strength to deal with such complex problems and becoming promising tools due to their ability in modelling of nonlinear process. This study will be helpful to enhance the frontiers for new research in the domain of hydrology. Further future research need to be explored towards the extraction of the knowledge that is contained the connection weights of the selected trained ANN models and also researchers should focus on selection of optimal number of input for the development of ANN models.
Keywords
Artificial Neural Networks, Streamflow, Time Series.References
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