Performance comparison of artificial neural network models for daily rainfall prediction

Devi, S. Renuga; Arulmozhivarman, P.; Venkatesh, C.; Agarwal, Pranay

doi:10.1007/s11633-016-0986-2

Cited by 42 publications

(12 citation statements)

References 22 publications

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“…An artificial neural network (ANN) is based on learning by decreasing errors during a training period; once training has been completed, the objective functions should be optimized (Devi et al 2016;Siva et al 2017;Chill and Lin 2006;Sasikumar and Harinandan 2014).…”

Section: Artificial Neural Network-based Direct Torque Controllermentioning

confidence: 99%

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Benayad

Gasbaoui

Bentouba

et al. 2020

Int. J. Renew. Energy Dev.

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Vehicle autonomy presents the most complex problem for modern commercialized solar electric vehicle (SEV) propulsion systems. The power supplied by electric vehicles’ batteries is limited by the state of charge, the type of battery, and its level of technological development. This study’s aim was to resolve the problem of energy variation at several velocities and under different road topology conditions. Several works related to the use of fuzzy logic confirm that classical regulators have such advantages over fuzzy regulators as short processing times and mathematical precision. Therefore, the hybrid power source is presented as the best solution for energy management, and it is composed of a solar panel (PV) and a nickel metal hydride battery. The PV system is connected to the SEV via a boost converter that is controlled using the maximum power point tracking technique. In this paper, we used an intelligent PI regulator for direct torque control, which introduced a certain degree of intelligence into the regulation strategy. Indeed, this approach of associating the PI regulator with the fuzzy rules-composed supervisor allowed us to take advantage of both the PI’s mathematical precision and the adaptability, flexibility, and simplicity of fuzzy linguistic formalism. Because of its dynamic capabilities, an adaptive PI regulator was substituted to achieve high speeds and a satisfactorily vigorous performance while quickly compensating for the disturbances that were expected to possibly take place on the regulation chain. The present study’s results confirm that the proposed control approach increased the utility of SEV autonomy under several speed variations. Moreover, the industry’s future offerings must take the option of hybrid power management into consideration during this type of vehicle’s manufacturing phase

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Section: Artificial Neural Network-based Direct Torque Controllermentioning

confidence: 99%

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Benayad

Gasbaoui

Bentouba

et al. 2020

Int. J. Renew. Energy Dev.

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“…Devi et al [10] compared the performance of ANN models for rainfall time series data prediction. Several ANN models, such as feed forward back propagation neural network (BPN), cascade forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN), and NARX network, were adopted to predict rainfall datasets in Nilgiris and Coonoor.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of the Mechanical Behavior of a Fuel Cell Stack under Strengthened Road Vibrating Conditions

Hou

et al. 2021

Modelling and Simulation in Engineering

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In this paper, a data-oriented model has been presented by nonlinear autoregressive exogenous model (NARX) neural network, which aims at predicting the mechanical behavior of a fuel cell stack for vehicle under the real-life operational conditions. A 300-hour vibration test with reproduction of SVP road spectrum was completed on a Multi-Axial Simulation Table. At the same time, data acquisition of drive displacement and acceleration response on stack was carried out in every 50 hours. All data collected were used to train and evaluate the model based on NARX. Result shows that the prediction model built is of good precision and consistent with the actual situation.

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“…Daily quantitative precipitation prediction is more challenging in rainfall prediction in particular. Devi et al [29] used different neural network models, such as Feed Forward BPk, Cascade Forward BP, Time delay neural network and Nonlinear Autoregressive Exogenous neural network (NARX), to predict rainfall one day in advance, and compared their forecasting capabilities. Dhar et al [16] developed a DNN to achieve high performance and accuracy compared to the old conventional ways of forecasting the weather.…”

Section: Introductionmentioning

confidence: 99%

An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction

Peng¹,

Zhao²,

Zhang³

et al. 2019

IJCIS

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Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches. This work presents a scheme to reduce the risk of Extreme Learning Machine (ELM) modeling error using Gene Expression Programming (GEP) to improve the prediction performance, and develops an ELM-GEP hybrid model for regional daily quantitative precipitation prediction. In this study, firstly, we use ELM for modeling the data sample of daily rainfall to construct a main model. Secondly, we use GEP for modeling the error of the main model as a compensation of the main model to reduce the prediction error. We conducted eight experiments of two different types of daily precipitation prediction problems using five metrics to evaluate our proposed model performance. Experimental results show that our model is comparable or even superior to five state-of-the-art models with high reliability in terms of all metrics on all datasets. It indicates that the proposed method is a promising alternative prediction tool for higher accuracy and credibility of regional daily precipitation prediction.

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Performance comparison of artificial neural network models for daily rainfall prediction

Cited by 42 publications

References 22 publications

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Prediction Model of the Mechanical Behavior of a Fuel Cell Stack under Strengthened Road Vibrating Conditions

An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction

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