Predicting lake wave height based on regression classification and multi input–single output soft computing models

Comment on os-2021-84

“…The results of this study are in strong agreement with those observed by Meng et al (2021) and Wei (2021) that each found that AI was highly effective at predicting hurricane-induced SWHs. However, although contemporary applications of AI in the forecasting of both in mean and extreme (i.e., TC-forced) waves states have relied traditionally on singular inputs of SWH (Ali and Prasad, 2019;Zhao and Wang, 2018;Zhou et al, 2021a, b), a growing body of literature have demonstrated that the addition of other variables such as wind speed (as done here), wind direction and other variables improves forecast effectiveness (Kaloop et al, 2020;Zubier, 2020;Raj and Brown, 2021;. Uncertainties in variable selection have also stimulated research into how to best identify predictors for the SWH or other predictands (Li and Liu, 2020;.…”

“…Ordinarily, momentum and mechanical energy are transferred to the ocean's surface from the overlying atmosphere, giving rise to ubiquitous surface gravity waves and other phenomena, under forcing by tropical cyclones (TC), these waves become extreme. As such, the study of TC-induced extreme significant wave heights (SWH) is at the current forefront of research and is traditionally accomplished by using an array of numerical models (Shao et al, 2019;Chao et al, 2020;Hu et al, 2020). However, although hindcasting, nowcasting, and forecasting (Alina et al, 2019;Cecilio and Dillenburg, 2020) can be https://doi.org/10.5194/os-2021-84 Preprint.…”

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

“…The results of this study are in strong agreement with those observed by Meng et al (2021) and Wei (2021) that each found that AI was highly effective at predicting hurricane-induced SWHs. However, although contemporary applications of AI in the forecasting of both in mean and extreme (i.e., TC-forced) waves states have relied traditionally on singular inputs of SWH (Ali and Prasad, 2019;Zhao and Wang, 2018;Zhou et al, 2021a, b), a growing body of literature have demonstrated that the addition of other variables such as wind speed (as done here), wind direction and other variables improves forecast effectiveness (Kaloop et al, 2020;Zubier, 2020;Raj and Brown, 2021;Wang et al, 2021). Uncertainties in variable selection have also stimulated research into how to best identify predictors for the SWH or other predictands (Li and Liu, 2020;.…”

“…Ordinarily, momentum and mechanical energy are transferred to the ocean's surface from the overlying atmosphere, giving rise to ubiquitous surface gravity waves and other phenomena, under forcing by tropical cyclones (TC), these waves become extreme. As such, the study of TC-induced extreme significant wave heights (SWH) is at the current forefront of research and is traditionally accomplished by using an array of numerical models (Shao et al, 2019;Chao et al, 2020;Hu et al, 2020). However, although hindcasting, nowcasting, and forecasting (Alina et al, 2019;Cecilio and Dillenburg, 2020) can be https://doi.org/10.5194/os-2021-84 Preprint.…”

Predicting Probability of Liquefaction Susceptibility Based on a Wide Range of CPT Data

“…To assess the performance of the models, the widely used performance indices, namely bias factor (BF), mean absolute error (MAE), mean absolute percentage error (MAPE), mean bias error (MBE), normalized mean bias error (NMBE), Nash-Sutcliffe efficiency (NS), performance index (PI), coefficient of determination (R 2 ), root mean square error (RMSE), variance account for (VAF), Willmott`s index of agreement (WI), and weighted mean absolute percentage error (WMAPE) are determined (Kaloop et al, 2019(Kaloop et al, , 2018Kaloop et al, 2020aKaloop et al, , 2020bKardani et al, 2021b, Ghani et al 2021Bardhan et al 2021b, a). The mathematical expressions of these performance indices are given below:…”