Implications for megathrust earthquakes and tsunamis from seismic gaps south of Java Indonesia.

Relocation of earthquakes recorded by the agency for meteorology, climatology and geophysics (BMKG) in Indonesia and inversions of global positioning system (GPS) data reveal clear seismic gaps to the south of the island of Java. These gaps may be related to potential sources of future megathrust earthquakes in the region. To assess the expected inundation hazard, tsunami modeling was conducted based on several scenarios involving large tsunamigenic earthquakes generated by ruptures along segments of the megathrust south of Java. The worst-case scenario, in which the two megathrust segments spanning Java rupture simultaneously, shows that tsunami heights can reach ~ 20 m and ~ 12 m on the south coast of West and East Java, respectively, with an average maximum height of 4.5 m along the entire south coast of Java. These results support recent calls for a strengthening of the existing Indonesian Tsunami Early Warning System (InaTEWS), especially in Java, the most densely populated island in Indonesia

Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms

The conventional experimental methods to determine biomass heating value are laborious and costly. Numerous correlations to estimate biomass' higher heating values have been proposed using proximate analysis. Recently, the utilisation of artificial neural network (ANN) has been extensively applied to predict HHV. However, most studies of ANN to estimate the biomass’ HHV only use one algorithm to train a small number of biomass datasets. The specific objective of this study is to predict the HHV of 350 samples of biomass from the proximate analysis by developing an ANN model which was trained with 11 different algorithms. This study fills a gap in the research on how to predict the HHV of biomass using numerous ANN training algorithms utilising sizeable biomass datasets. Results show that the ANN trained with Levenberg-Marquardt gave the highest accuracy. The Levenberg–Marquardt algorithm shows the best fit giving the highest R and R2 values and the lowest MAD, MSE, RMSE and MAPE. Compared with previous biomass HHV prediction studies, the ANN model developed in this study provides improved prediction accuracy with higher R2 and lower RMSE. Results from this study have also indicated that the Levenberg-Marquardt should be the first-choice supervised algorithm for feedforward-backpropagation

Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine: Review of ANN for gasoline, diesel and HCCI engine

In automotive applications, artificial neural network (ANN) is now considered as a favorable prediction tool. Since it does not need an understanding of the system or its underlying physics, an ANN model can be beneficial especially when the system is too complicated, and it is too costly to model it using a simulation program. Therefore, using ANN to model an internal combustion engine has been a growing research area in the last decade. Despite its promising capabilities, the use of ANN for engine applications needs deeper examination and further improvement. Research in ANN may reach its maturity and be saturated if the same approach is applied repeatedly with the same network type, training algorithm and input–output parameters. This review article critically discusses recent application of ANN in ICE. The discussion does not only include its use in the conventional engine (gasoline and diesel engine), but it also covers the ANN application in advanced combustion technology i.e., homogeneous charge compression ignition (HCCI) engine. Overall, ANN has been successfully applied and it now becomes an indispensable tool to rapidly predict engine performance, combustion and emission characteristics. Practical implications and recommendations for future studies are presented at the end of this review