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The present investigation aims to find out the performance of the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting the earthquake magnitude. More specifically, a comparison is made between ANN and ANFIS, to check their efficiency in predicting earthquake magnitude. For this purpose, 47 data sets of the earthquake are collected from the period of 1906 to 2019. Time of occurrence, latitude, longitude, and focal depth of the earthquake is considered the input variables. Since there is no mathematical and empirical relationship between these variables to predict the earthquake magnitude, the problem is modelled using two different soft computing tool, i.e. ANN and ANFIS. Grid partitioning and subtractive clustering algorithms are employed to develop the model in ANFIS. The result showed that ANN is more efficient in predicting the earthquake magnitude when compared to ANFIS. Adopting this technique in predicting the earthquake magnitude is found to be very fast and economic.

The present study proposes an empirical technique for the prediction of tentative yield expected from a reservoir propounded at any site in the study area. The proposed methodology was validated in two parts considering India as the case study region. In the first part, seventeen major river catchments of the country were examined to establish an empirical relationship between peak flow rate and average flow rate. The former was calculated using the rational formula while the latter was acquired from the WRIS portal. The generated equation provided the average streamflow value by using only the rainfall data of an aspiring reservoir location. In the second part of the validation, a mathematical model was established for computing the yield from a reservoir. The dependent variables were computed using the streamflow dataset of 212 sites, spread widely, across the country’s geography. The results of the present study may prove to be beneficial for the reconnaissance planning of a reservoir. The proposed methodology may also be applied to other countries/regions to achieve similar results.

This study aims to better understand the empirical relationship between porosity and rock density. The illustration represents this relationship that is used for matrix density determination. The core analysis, of 78 samples of the Miocene Rudeis sandstone of land reservoir and 138 samples from the marine reservoir of Belayim field, Gulf of Suez, Egypt, has been used in this article. The data include dry weight, bulk volume, bulk density, and porosity. In this study, accurate evaluation of the matrix density values for the studied reservoir, taking into consideration the cement distribution effect, is necessary. This is achieved through the constructed chart between the laboratory-measured porosity and the bulk density of the available core samples for the interested reservoir. The true matrix density is located at porosity equals to zero. The porosity-density equation is used to estimate the total porosity.