In this study, the wave conditions in the Arabian Sea induced by tropical cyclone Kyarr (2019) have been simulated by employing the 3rd generation wave model MIKE 21 SW. The model was run from 24 October to 1 November 2019, a total of 8 days. The MIKE 21 SW model was forced by reanalyzed ERA5 wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with buoy data from the Indian National Centre for Ocean Information Services (INCOIS), which is located at 67.44° E, 18.50° N. In addition, the satellite altimeter data (CryoSat-2, SARAL and Jason-3 satellite altimeter data) was utilized for validation. Three wave parameters are considered for the validation: the significant wave height; the peak wave period; and the mean wave direction. The validation results showed that the significant wave height, the peak wave period, and the mean wave direction could be reasonably predicted by the model with reanalysis wind data as input. The maximum significant wave height reached to 10.7 m (with an associated peak wave period of 12.5 s) on 28 October 2019 at 23:00:00 in the middle of the Arabian Sea. For coastal areas, the significant wave height along the Iran and Pakistan (north Arabian Sea) coasts increased to a range of 1.4–2.8 m when tropical cyclone Kyarr moved northward. This wave height along with elevated sea level may cause severe coastal erosion and nearshore inland flooding. Impacts of cyclones on coastal zones critical facilities and infrastructure can be reduced by timely and suitable action before the event, so coastal managers should understand the effect of cyclones and their destructive consequences. The validated model developed in this study may be utilized as input data of evaluating the risk to life and infrastructure in this area.

The utilization of bio-hydrogen as a fuel source holds immense promise as a renewable energy option, offering compelling economic and environmental advantages. This study investigates the economic and environmental advantages of bio-hydrogen as a renewable energy source compared to fossil fuels, focusing on the reduction of greenhouse gas emissions such as carbon dioxide and carbon monoxide. The enhancement of anaerobic hydrogen production reactor capacity is explored through the application of a fuzzy controller system. Numerical simulations demonstrate that the fuzzy controller outperforms other methods in augmenting biological hydrogen production, effectively addressing the inherent non-linear characteristics of the system. In contrast, limitations in robustness against system uncertainty are observed with the non-linear controller. Exceptional tracking of desired values by the fuzzy controller, even in the presence of model uncertainty, results in a lower integral of time multiplied by squared error (ITSE) performance index compared to non-linear and proportional–integral controllers. Emphasizing the viability of the fuzzy method for regulating hydrogen production processes, potential gains of up to 95% in biological hydrogen production are indicated compared to open-loop configurations. This clean-burning fuel holds promise for industrial applications, contributing to the reduction of harmful gas emissions. The findings underscore the transformative potential of the fuzzy controller system in advancing sustainable hydrogen production and its significant role in addressing environmental concerns.

In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80 of the available data, while the test state uses 20. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.