Spatial interpolation is a required method to generate a continuous surface such as Digital Elevation Model (DEM) because field investigation for most of the surface’s part is time-consuming with a high demand in both human resources and monetory cost. One of the most used deterministic interpolation models is Inverse Distance Weighting (IDW) model. The model takes several neighbors’ information, and the weights are constructed based on the distance between the interpolated point and the neighbors’ points. From the machine learning model, Artificial Neural Networks (ANNs) model has also been used for spatial interpolation. The input of ANNs model is also one of the parameters that need to be defined when building the model. This paper evaluated the effect of the number of inputs (neighbors) on the elevation interpolation accuracy. We applied IDW and ANNs to interpolate the elevation of Balikpapan City, Indonesia. The results show that the accuracy increases significantly when the number of inputs is between one and three. However, after three inputs, additional input would not change the accuracy significantly. ANNs performed better than IDW. For three or more inputs, the MAE of ANNs and IDW interpolations are below 1.1 and around 2 meters, respectively.

Generally, Technical and Vocational Education and Training (TVET) has numerous types of assets used for conducting various skill-based learning activities. The failure to properly manage the TVET assets can lead to inefficient operation and administration, such as difficulty in tracking the history of the asset, location, and users. The research solved the problems by developing and implementing asset management and tracking system based on ubiquitous cloud computing for movable and fixed assets. The research activities were conducted in Politeknik Negeri Balikpapan, one of ten state-owned TVET institutions in Indonesia. This system was accessed using a web and mobile application. Quick Response (QR) code was used for asset identification to make a mobile device read the code with their built-in camera. Meanwhile, the geolocation was attached to provide the spatial information of assets’ whereabouts. Then, the research adopted the 5W1H question principle, so all aspects of asset management were collected and understood. The results show that the system helps TVET to keep track of their equipment and vital inventories in realtime. Moreover, the implementation of the system has a great impact administratively and ease in delivering instantaneous data and assets history for decisionmaking to internal and external asset auditors.

In several previous studies on flood analysis and estimation, there was no clear rationale for why different researchers used a different combination of parameters in the determination of flood zones. Such research results raise the question of how to select a few dominant parameters without reducing the objectivity of the analysis. This research proposes the standardization of parameters selection by using Pareto Analysis in screening a few vital flood parameters from numerous parameters that prevail in certain areas. The selection of the right dominant parameters is the key to achieving the analysis goal and it will also simplify the analysis processes. This flood zone estimation study uses a combination of Pareto Analysis, Analytic Hierarchy Process (AHP) and Geographic Information System (GIS). The results of the study include a flood zonation map. The study area can be classified by its level of vulnerability as follow: very low vulnerability zones (0.003 km2), low vulnerability zones (5.588 km2), medium vulnerability zones (11.876 km2), high vulnerability zones (8.629 km2), and very high vulnerability zones (2.198 km2). The validation shows that the estimation of the most vulnerable zone is consistent with field validation and the flood event history of several locations in the study area. As a result, the developed model can provide an accurate flood zonation map, enabling stakeholders to take appropriate mitigation measures for different areas.

The infiltration rate of soil data is important in a wide range of planning, such as city planning, drainage design, landuse planning, flood prediction, flood disaster mitigation, etc. Collecting data of infiltration through in-site direct measurements is time consuming and costly. Indeed, inferring the infiltration rate using available parameters and the fittest model is needed. The model can shortcut the field measurement to get a predicted accurate infiltration rate that is worthy to support vital planning. This research aims to develop a model of infiltration rate based on initial water contents and grain size of soils. The results are three outstanding models based on the Multiple R Squared, Root Mean Square Error (RMSE), and Mean Average Error (MAE). The implication of the fittest model is reducing the cost and time to get the predicted infiltration rate. The field measurements can be skipped by sampling undisturbed soils and laboratory tests. Keywords: infiltration rate, initial water contents, grain size

Carbon Forests Projects (CFPs) play a significant role in climate change mitigation strategies. Implementation of such projects should bring environmental benefits to companies, as well as contribute to improving the environmental management efficiency in the region. The aim of this paper is to analyze the possibility of developing CFPs as a tool to achieve carbon neutrality, taking into account its environmental and economic benefits. The analysis is undertaken on the example of the Bystrinsky Mining and Processing Plant (MPP), located at the Zabaykalsky Krai (southern Siberia, Russia). The ability of the forest lands to absorb greenhouse gas emissions is assessed using the ROBUL methodology. An important feature of CFPs is the ability to combine a positive environmental effect by contributing to climate change mitigation with strengthening the image of an environmentally friendly company. However, the analysis of CFPs implementation in the example of Bystrinsky MPP shows the excess of costs over benefits. In this regard, more effective projects can be proposed for regions with a high degree of forest cover, such as Zabaikalie. Reducing the risk of forest fires in such regions can have a more tangible environmental impact by preventing additional CO2 emissions. Identifying and comprehensively assessing the environmental and economic impacts of CFPs in the context of carbon neutrality facilitates the selection of the most relevant solutions.