Artificial Neural Networks for developing Early Warning System for Banking System: Indian context

2022 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Neha Gupta ◽  
Arya Kumar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Warning ◽  
Early Warning System ◽  
Banking System ◽  
Warning System ◽  
Indian Context ◽  
Artificial Neural

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

scholarly journals Estimation of Rainfall from Climatology Data Using Artificial Neural Networks in Palembang City South Sumatera

2021 ◽  
Vol 930 (1) ◽  
pp. 012062
Author(s):  
E Suhartanto ◽  
S Wahyuni ◽  
K M Mufadhal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Warning ◽  
Rainfall Data ◽  
Climatological Data ◽  
Data Requirement ◽  
Floods And Droughts ◽  
Artificial Neural

Abstract Estimation of climatological parameters, especially rainfall is a data requirement for all regions of Indonesia. The availability of rainfall data is used for early warning of flood or drought disasters. The study location is in Palembang City, South Sumatra Province, where floods and droughts often occur and lack of availability of rainfall data. This study aims to obtain the best model in estimating rainfall from climatological data. The analysis was carried out to estimate the rainfall from the climatological data using the Artificial Neural Networks method. The Artificial Neural Networks were applied and showed some results with the best calibration was at 16 years using TRAINLM with 1500 epochs that is the performances NSE = 0.54, RMSE = 99.37, and R = 0.74. Whereas the best validation was at 1 year that is the performances NSE = 0.41, RMSE = 87.32, and R = 0.65.

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

scholarly journals Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks

2020 ◽  
Vol 82 (9) ◽  
pp. 1921-1931
Author(s):  
Ming Wei ◽  
Lin She ◽  
Xue-yi You
Keyword(s):  
Neural Networks ◽  
Early Warning ◽  
Early Warning System ◽  
Low Impact Development ◽  
Warning System ◽  
Extreme Rainfall ◽  
Area At Risk ◽  
Risk Area ◽  
Rainfall Process ◽  
Flooding Duration

Abstract The optimal layout of low-impact development (LID) facilities satisfying annual runoff control for low rainfall expectation is not effective under extreme rainfall conditions and urban waterlogging may occur. In order to avoid the losses of urban waterlogging, it is particularly significant to establish a waterlogging early warning system. In this study, based on coupling RBF-NARX neural networks, we establish an early warning system that can predict the whole rainfall process according to the rainfall curve of the first 20 minutes. Using the predicted rainfall process curve as rainfall input to the rainfall-runoff calculation engine, the area at risk of waterlogging can be located. The results indicate that the coupled neural networks perform well in the prediction of the hypothetical verification rainfall process. Under the studied extreme rainfall conditions, the location of 25 flooding areas and flooding duration are well predicted by the early warning system. The maximum of average flooding depth and flooding duration is 16.5 cm and 99 minutes, respectively. By predicting the risk area and the corresponding flooding time, the early warning system is quite effective in avoiding and reducing the losses from waterlogging.

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