A Comparison Review based on Classifiers and Regression Models for the Investigation of Flash Floods

Abstract

Several regions of the world have been affected by one of the natural disasters named as flash floods. Many villagers who live near stream or dam, they suffer a lot in terms of property, cattle and human lives loss. Conventional early warning systems are not up to the mark for the early warning announcements. Diversified approaches have been carried out for the identification of flash floods with less false alarm rate. Forecasting approaches includes some errors and ambiguity due to the incompetent processing algorithms and measurement readings. Process variables like stream flow, water level, water color, precipitation velocity, wind speed, wave's pattern and cloud to ground (CG) flashes have been measured for the robust identification of flash floods. A vibrant competent algorithm would be required for the investigation of flash floods with less false alarm rate. In this research paper classifiers have been applied on the collected data set so that any researcher could easily know that which classifier is competent and can be further enhanced by combining it with other algorithms. A novel comprehensive parametric comparison has been performed to investigate the classification accuracy for the robust classification of false alarms. For the better accuracy more than one process variables have been measured but still contained some false alarm. Appropriate combination of sensor was integrated to increase the accuracy in results as multi-modal sensing device has been designed to collect the data. Linear discriminant analysis, logistic regression, quadratic support vector machine, knearest neighbor and Ensemble bagged tree have been applied to the collected data set for the data classification. Results have been obtained in the MATLAB and discussed in detail in the research paper. The worst accuracy of the classification (62%) has been achieved by the coarse k-NN classifier that means coarse k-NN produced 38% false negative rate that is not acceptable in the case of forecasting. Ensemble bagged trees produced best classification results as it achieved 99 % accuracy and 1% error rate. Furthermore, according to the comprehensive parametric comparison of regression models Quadratic SVM found to be the worst with mean square error of 0.5551 and time elapsed 13.159 seconds. On the other hand, Exponential Gaussian process regression performed better than the other existing approaches with the minimum root mean squared error of 0.0002 and prediction speed of 35000 observations per second.

High-frequency CSI300 futures trading volume predicting through the neural network

“…These three algorithms have been demonstrated by previous studies in terms of their success in achieving relatively good accuracy under various circumstances (e.g. Doan and Liong, 2004;Kayri, 2016;Khan et al, 2019;Selvamuthu et al, 2019;Xu and Zhang, 2021a, d, 2022a, c, 2023b. Baghirli (2015) and Al Bataineh and Kaur (2018) have carried out targeted studies comparing these three algorithms.…”

Yellow corn wholesale price forecasts via the neural network

“…One is the LM (Levenberg–Marquardt) algorithm (Levenberg, 1944; Marquardt, 1963) and the other is the SCG (scaled conjugate gradient) algorithm (Møller, 1993). These two algorithms have witnessed wide successful applications for forecasting purposes from different research areas (Doan & Liong, 2004; Kayri, 2016; Khan, Alam, Shahid, & Mazliham, 2019; Selvamuthu, Kumar, & Mishra, 2019; Xu & Zhang, 2021, Xu & Zhang, 2021, Xu & Zhang, 2022, Xu & Zhang, 2022, Xu & Zhang, 2022, Xu & Zhang, 2022). Their comparisons have been illustrated in previous research (Al Bataineh & Kaur, 2018; Baghirli, 2015; Xu & Zhang, 2022, Xu & Zhang, 2022, Xu & Zhang, 2022).…”

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“…Here, we also take into consideration the scaled conjugate gradient (SCG; Møller, 1993) and Bayesian regularization (BR; MacKay, 1992; Foresee and Hagan, 1997) algorithms. The SCG and BR algorithms, as well as the LM algorithm, have been explored in many different varieties of fields (Xu and Zhang, 2022a, 2023a; Doan and Liong, 2004; Xu and Zhang, 2022n; Kayri, 2016; Xu and Zhang, 2022p; Khan et al , 2019; Xu and Zhang, 2023c; Selvamuthu et al , 2019; Xu and Zhang, 2021a). Comparative studies of these algorithms could be seen from, e.g.…”