Altitude Measurement in Topographic Mapping Based on Barometric Pressure, Temperature, and Humidity using Neural Network

Abstract

The Global Positioning System (GPS) has important information and cuts across various sectors. The position of a particular spot is usually stated in coordinates (2D or 3D) based on a specific coordinate system. Simple methods of altitude measuring have been developed in topographic mapping. This study developed an altitude measurement tool using a BMP180 sensor and DHT22 sensor, with calculation from an artificial neural network (ANN) result, based on the influence of the amount of barometric pressure, temperature, and humidity. Output can be displayed through an LCD and a smartphone application, enabled through Bluetooth. The ANN for obtaining altitude values was trained using temperature, humidity, and barometric pressure inputs from places with known high values. The training was conducted in MATLAB. Afterward, the ANN test program Arduino used normalization, denormalization, activation, weight, and bias components obtained from the selected ANN architecture. The Arduino test program showed high output values similar to those from the ANN test, indicating that the test program result is correct. The test results obtained an average error of 6.36%. The advantage of this tool is that it can perform height calculations quickly and easily. Moreover, the tool can further be developed, as training the ANN in various places with more variations in position, height, weather conditions, or height can yield better results.