One of the parameters characterizing the quality of the gaseous fuel transported in gas pipeline network to consumers and being the basis for the classification of gaseous fuels is the heat of combustion. The main research hypothesis of this paper is the analysis of the possibility of using MLP 18-yi-1 neural network model to forecast the natural gas heat of combustion with a forecast error smaller than in case it calculates the heat of combustion based on the composition of natural gas predicted using the MLP 18-65-5 (Szoplik and Muchel, 2023). The training of the models was carried out on the basis of 8760 real data, presenting the hourly heat of natural gas combustion at one of the measurement points of this parameter in the pipeline network. The model takes into account the influence of calendar factors (month, day of the month, day of the week and hour of the day) and weather factors (ambient temperature) on the amount of heat of natural gas combustion in a given location of the gas network. Many MLP 18-yi-1 models were trained, differing in the number of neurons in the hidden layer and activation functions of neurons in the hidden and output layers.

The applicability of integratedUnmannedAerialVehicle (UAV)-photogrammetry and automatic feature extraction for cadastral or property mapping was investigated in this research paper. Multi-resolution segmentation (MRS) algorithm was implemented on UAVgenerated orthomosaic for mapping and the findings were compared with the result obtained from conventional ground survey technique using Hi-Target Differential Global Positioning System (DGPS) receivers. The overlapping image pairs acquired with the aid of a DJI Mavic air quadcopter were processed into an orthomosaic using Agisoft metashape software while MRS algorithm was implemented for the automatic extraction of visible land boundaries and building footprints at different Scale Parameter (SPs) in eCognition developer software. The obtained result shows that the performance of MRS improves with an increase in SP, with optimal results obtained when the SP was set at 1000 (with completeness, correctness, and overall accuracy of 92%, 95%, and 88%, respectively) for the extraction of the building footprints. Apart from the conducted cost and time analysis which shows that the integrated approach is 2.5 times faster and 9 times cheaper than the conventional DGPS approach, the automatically extracted boundaries and area of land parcels were also compared with the survey plans produced using the ground survey approach (DGPS) and the result shows that about 99% of the automatically extracted spatial information of the properties fall within the range of acceptable accuracy. The obtained results proved that the integration of UAVphotogrammetry and automatic feature extraction is applicable in cadastral mapping and that it offers significant advantages in terms of project time and cost.