Abstract
Geoelectrical resistivity data collected from the ground contain lot of noises and errors. It requires efficient algorithm to reduce the errors to make an actual inversion models. Though different algorithm can be applied, nature inspired algorithm is more potential in inverting geoelectrical data in an elegant and comprehensive way. Bargain Optimization (BO) algorithm is framed on the concept of bargaining things to purchase for needs. In general, effective bargaining results in more profit and leads to loss when it fails. In this research work, Bargain Optimization algorithm is applied to invert geoelectrical data and the effective bargaining will take time to process and to obtain the required model. The input data is AB/2, apparent resistivity data and the inverted model through BO algorithm is successfully matched with the available litholog section of the study area. The output graphs have profit/loss bar graph, which reveals the status of bargaining during a particular number of epochs.
Author Contributions
Copyright© 2021
Raj Stanley, et al.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Competing interests The authors have declared that no competing interests exist.
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Introduction
Groundwater plays vital role in our ecosystem as it replenishes lakes, rivers wetlands etc., and used for principal source of drinking water and it is also utilized for industrial and agricultural purposes. The significant escalation of human activities and various reasons such as climate change, the global groundwater resources are under large stress. The stable advancement of various geophysical techniques with the substantial usage of different physical properties for the application of ground water exploration are electrical resistivity, magnetic susceptibility, elasticity, density and radioactivity The Electrical resistivity method has usually been employed in determining the model parameters of the subsurface of our Earth The process of optimization is one of the best techniques to evaluate the results. Basically, optimization involves in minimizing the errors between the both anticipated and observed results within the peculiar constraints. Several researchers applied neural networks coupled with other optimization algorithm to produce favorable results Artificial neural networks have independent-learning competence and are of noise-immune and founds applications in numerous fields
Results
Intelligent data analysis can interpret geophysical data with accurate and plausible results. Though the geophysical parameter involves lot of noises and errors, intelligent data analysis can filter and manage the data to provide optimized solution. Geoelectrical data is one of the such kind with noises from heterogeneous media of earth. This errors and noises will suppress the original sub surface geology of the data. The values of MSE, PSNR, R- Value, RMSE (Root Mean Square Error), NRMSE, MAPE, Computational Time. The comparison of the performance function from different algorithm with the Bargaining Optimization algorithm is stated below. In General, the mean squared error (MSE) of an optimization technique in statistics calculates the sum of the squares of the errors—that is, the average squared discrepancy between the expected and real values. Where, N – Number of training data The MSE value of Feedforward Network is much higher when compared to other algorithms. The MSE obtained from Generalized Regression Neural Network is 0.10 representing that the algorithm is much accurate than other techniques. BO algorithm is the second most accurate technique which states that it is better for prediction. Peak signal-to-noise ratio (PSNR) is an equation for the ratio of a signal's highest potential value (power) to the power of altering noise that influences the accuracy of its representation. According to the obtained PSNR values, Generalized Regression Neural Network has the highest value of PSNR ratio with 57.9. The coefficient of correlation is denoted by the letter R. It indicates how well the expected outputs align with actual outputs, with R close to 1 indicating a good qualified network and 0.2 and 0.3 indicating a poor network. The R values of all the algorithm are nearly equal to 1. The root of the mean square error value gives the RMSE values. RMSE has never been negative, and a value of 0 (which is almost never obtained in reality) indicates a great match to the results. In general, a lower RMSE is preferable to a higher RMSE Where, P = number of output processing elements MSE- Mean Squared error Since MAPE is a calculation of error, higher values are weak and lower values are great. Where, At – Actual Value Ft – Forecasted Value The MAPE value we got for BO algorithm is less than 10 percentage, which shows that the technique is very much accurate. Whereas the Feedforward and probabilistic neural network has the higher values of MAPE which corresponds to less accuracy. The amount of time needed to complete a computing task is referred to as computation time. The computational time for feedforward technique is more time taking whereas the Bargaining Optimization Algorithm is the fastest algorithm which gives the most approximate outcome.