Combination of Decision Tree and K-Means Clustering Methods for Decision Making of BLT Recipients in the Covid-19 Period

The economic conditions during the Covid-19 outbreak had an impact on society globally. The number of people who have experienced layoffs has an impact on the economic conditions of the family. The economic impact that helps the community encourages the government to increase efforts to increase social assistance in the form of BLT. However, the distribution of BLT was not right on target, there were still many people who really could not afford not to receive BLT, while those who were still able to get BLT assistance. Therefore, it is important in this study to use a combination of the K-Means Cluster and Decision Tree methods to be used in BLT recipient decision making, with the aim of increasing BLT recipients as expected. The calculation results were obtained using a combination of the K-Means Cluster and Decision Tree methods referring to the criteria for the community who has the right to receive data with an error level of -2.476190476 <from error tolerance 6.84.

An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.