Prediction of temperature in 2 meters temperature probe survey in Blawan geothermal field using artificial neural network (ANN) method

Research on temperature gradient has been carried out in Blawan geothermal area. This study aims to predict the temperature in the subsurface temperature measurement using a temperature probe with a depth of 2 m in the Blawan geothermal area. Temperature and depth are the two variables being measured. Meanwhile, the resistivity, conductivity, and humidity data were taken from previous studies in the exact area measurements. The prediction determination used modeling with an Artificial Neural Network (ANN) with the back-propagation method. The optimal predictions using an Artificial Neural Network (ANN) were obtained by constructing three input layers, five hidden layers, and two output layers (3-5-2) with a hyperbolic tangent function. Results for temperature prediction with the larger R2 (1) values and lower MAPE (1.07%), RMSE (0.78), MSE (0.61), and MAD (0.34) values. Moreover, humidity generates a greater R2 (1) values and lower MAPE (0.34%), RMSE (0.34), MSE (0.18), and MAD (0.29) values. ANN proved very effective in predicting temperature and humidity factors.

Automated summary

This study used an Artificial Neural Network (ANN) to predict the subsurface temperature and humidity in the Blawan geothermal area, showing that the method is highly accurate and effective.