Flash Flood Characteristics of Ciberang River, Its Impact and Mitigation

Flash flood is a deadly natural disaster that develops at space and time scales caused by high rainfall intensities and frequently occurs. It occurs in mountainous regions with steep slope relief and often causes a loss of economy, society, and environment and threatens human lives. The Ciberang river is located at Lebak district, Banten Province, and has been experiencing a significant flash flood from December 31, 2019 to January 01, 2020, which contributed to various damages, including households nearby the river. In this study, the impact areas were identified with the HEC-RAS model and satellite image data. The impact area was damage on eight sub-districts, including agriculture (77.86 Ha), buildings (0.80 Ha), roads (2.5 km), and nine bridges. In addition, this study tried to build Sabo Dam upstream of Banjar Irigasi sub-districts to respond to the flash flood occurrence. The results have reduced the discharge to around 40 m3/s, and the travel time was delayed about 53 min. The results of this study help the community and decision-makers be ready for further flash flood disasters.

The Bottom Surface Sediment Transport Changes in Bathymetry in the Rupat Strait, Riau Province, Indonesia

The Rupat Strait is located at the eastern coast of Sumatera Island, Riau Province,Indonesia, under the influence of the current system flowing from the Malacca Strait into the Strait.The primary purpose of this study is to identify the topographyof Rupat Strait prevailed by bathymetry changes, which was analyzed using oceanographic and satellite images.This study was conducted in the Rupat Strait in July 2018 through two steps of research: 1) oceanographic observation; 2) bathymetry measurement.The study of oceanographic observation was carried out along the coastal areas of Dumai City and Rupat Island. The depth of Rupat Strait (bathymetry) was measured using an echosounder at 60 stations in July 2018. The changes in the depth of the Strait were analyzed by comparing data between depth in 1990 by satellite images and the depth in 2018 by the measurement. The results of study indicate that dominantly, the depth of the Strait has increased by 1.5-2.7 meters for 28 years. The depth of the Rupat Strait, based on the interpretation of the satellite image data of MIKE C-MAP (1990) ranges from 0.9 to 29.4 meters, and of the measurement in 2018, ranging from 2.4 to 32.1 meters.The dominant factor causing the changes are the current system flowing from the Malacca Strait through the Strait during high and low tides, and the surface bottom sediments are transported out the Strait by the current, including the bottom current.

Analysis of the influence hybrid mass coordinate on WRF-ARW models to the turbulence simulation of Batik airlines aviation (case study October 24, 2017)

The influence of hybrid sigma coordinate is better to represent turbulence in America than basic sigma coordinate. Therefore, it is necessary to search the effect of these coordinates on turbulence simulations in Indonesia due to the analysis of different atmospheric conditions from America. In this research, two experiments are performed using two different vertical coordinates with a case study flight turbulence from Batik Airlines on October 24, 2017. The two different vertical coordinates are the hybrid sigma coordinate and basic sigma coordinate. The data used are NCEP-FNL, Himawari-8 satellite image data, and sounding data. Based on the result of this research, simulation using hybrid sigma coordinate shows isentropic lines that have the potential turbulence during and after turbulence event. Richardson number value about 0.1 – 0.2 and intensity of the energy dissipation rate is 0.06 m 2/3s-1. According to the Richardson number value and intensity of the energy dissipation rate, the hybrid sigma coordinate simulation shows turbulence potential more significant than the basic sigma coordinate.

House Price Prediction using Machine learning and Image Processing

We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.