National Research and Innovatuon Agency
Doctor on Environmental Science, Universitas Indonesia
Water Resource Management, Flood Risk Management, Environmental Science
The hydrological response to changes in Land Use Land Cover (LULC) requires a multidisciplinary approach. The process involves identifying current LULC using remote sensing and GIS technology. Machine learning and deep learning algorithms can be used to analyze large-scale datasets and predict future LULC scenarios. Satellite precipitation data is essential for understanding hydrological processes and their relationship with LULC changes. Climate change models are incorporated to assess the impact of changing climatic conditions on the hydrological system. Hydrological modeling simulates water movement, accounting for factors like infiltration, evaporation, and runoff. By integrating the simulated hydrological response with digital elevation models, researchers can identify flood-prone areas and make informed decisions for land-use planning and disaster management.
Coastal areas face flood risks from land subsidence and rising sea levels. To manage these risks, monitoring land subsidence patterns is crucial. Satellite InSAR data provides detailed measurements of surface deformation, helping identify affected areas. Ground measurements validate satellite data accuracy. Predictive models based on historical data and machine learning techniques forecast future land subsidence rates and locations. Groundwater extraction contributes to subsidence, emphasizing the need for sustainable management practices. Geospatial analysis integrates subsidence data, sea level rise projections, flood maps, and vulnerability assessments. Machine learning algorithms identify high-risk areas and assess community resilience. Combining these insights improves early warning systems, land-use planning, infrastructure resilience, and adaptation measures. These efforts enhance safety, well-being, and sustainable development along the coast.
Dionysius Bryan Sencaki, Mega Novetrishka Putri, Budi Heru Santosa, Siti Arfah, Robby Arifandri, Afifuddin, Muhammad Iqbal Habibie, Prabu Kresna Putra, Nico Anatoly, Zilda Dona Okta Permata,et al. Elsevier BV
Budi Heru Santosa, Dwi Nowo Martono, Rachmadhi Purwana, Raldi Hendro Koestoer, and Wiwiek Dwi Susanti Springer Science and Business Media LLC
Anisah Anisah, Irwansyah Irwansyah, Agustan Agustan, Budi Heru Santosa, Oni B. Bintoro, Citra Hasna Paramita, Bambang Winarno, and Diana Emillia AIP Publishing
Agustan Agustan, Takeo Ito, Rachmadhi Purwana, Ruki Ardiyanto, Budi Heru Santosa, and Heri Sadmono IEEE
The initiatives of open data and open platforms, combined with on-demand data processing in Synthetic Aperture Radar (SAR), enable more straightforward and accessible ground deformation research worldwide. The Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC) provides high-quality interferogram combination pairs based on the Small Baseline Subset (SBAS) technique and on-demand InSAR data analysis known as HyP3. HyP3 offers unwrapped phase data from Sentinel-1 satellites and other derived products that can be utilized with open-source deformation software such as Mintpy. This research aims to assess the quality of interferogram combination pairs by configuring networks of perpendicular and temporal baselines for observing ground deformation in Semarang, Central Java, Indonesia. The study demonstrates that HyP3 products enable time series analysis to determine deformation rates. Over 8.4 years, from November 15th, 2014, to April 14th, 2023, the Simpang Lima landmark in Semarang experienced an approximate subsidence rate of -0.87 cm/year. However, the maximum subsidence rate of approximately 11 cm/year was observed in the southeastern part of Semarang.
E G A Sapan, B H Santosa, I Ridwansyah, M Fakhrudin, A E Pravitasari, R Novianti, F A Wardhani, S Abdiyani, N L Adhyani, and A M Setiawan IOP Publishing
Abstract Flash floods are catastrophic phenomena known for their rapid and unpredictable occurrence. They frequently display precursory indicators, which, when comprehensively studied, serve as vital clues for effectively anticipating and preparing for these events, thus reducing their adverse effects. This study aimed to investigate the key trigger factors contributing to a flash flood event in the upper Ciliwung Watershed in January 2021. Data collection involved various parameters, including morphology, precipitation patterns, land use, hydrological characteristics, and soil conditions collected from the affected area. These data were subsequently analyzed using geospatial methods and enriched with information from local community members. The analysis found multiple contributing factors to flash floods, including high soil moisture content due to prolonged precipitation, geological characteristics, and steep topography. The local community recognized landslides, creating a natural dam about a month before the flash flood. Unfortunately, inadequate measures were taken to address the flood due to limited understanding and resources. The heavy rainfall before the flash flood put immense pressure on the natural dam, causing it to fail and carry debris downstream. The steep banks of the surrounding river worsened the situation. The study’s findings can provide valuable insights for local authorities, leading to better resource allocation.
H Amrullah, K Amaru, I Ridwansyah, and B H Santosa IOP Publishing
Abstract On January 19, 2021, a flash flood hit the Upper Ciliwung watershed, a mountainous area in Indonesia, causing damage to seven buildings and displacing 1,800 residents. To minimize the impact of such disasters, there is a need for disaster risk awareness and management specifically focused on flash floods in mountainous regions. Therefore, this study aims to assess the flash flood potential index value in the Upper Ciliwung watershed. The research utilized deterministic (Soil and Water Assessment Tool-SWAT) and parametric (Flash Flood Potential Index-FFPI) modeling to analyze various factors, including slope, geology, ground movement, Antecedent Precipitation Index (API), and Runoff Coefficient (Curve Number-CN). The models showed satisfactory goodness-of-fit statistics with R2 and Nash-Sutcliffe efficiencies (NSE) values of 0.58 and 0.57, respectively. The findings indicated that 39.95% of the watershed had a moderate vulnerability index value of 3, while downstream areas (31.7%) had a low vulnerability index value of 2, and the middle and upstream regions had high vulnerability index values of 4 and 5, covering 23.65% and 2.49% of the total area. These results provide valuable insights to local authorities for implementing measures to reduce the Upper Ciliwung watershed’s vulnerability to flash floods.
B H Santosa IOP Publishing
Abstract During a flood event, flood-affected households need adequate flood resilience. The efforts to increase household flood resilience require an adequate understanding of the factors affecting household flood resilience. This study aimed to explore the factors influencing household flood resilience in three flood-affected sub-districts in Tangerang City, Indonesia. The composite indicator method was applied to process data on economic, home environment, social capital, institutional, communication and information, and flood risk perception, using questionnaire data from 354 flood-affected households as respondents and in-depth interviews with local leaders. The results showed that the composite indicator method worked well for measuring the household flood resilience index (HFRI); in the Gembor sub-district, HFRI was 2.88; in Gebang Raya, it was 3.12; and in Periuk, it was 3.03. Analysis based on economic conditions, flood depth, period of residence, and flood risk perception also showed variations in HFRI, which could determine methods to increase household flood resilience. In conclusion, the composite indicator method is an adequate tool to measure flood resilience despite it being an abstract object. Furthermore, the local government can use the HFRI to develop planning efforts to increase household flood resilience based on influencing factors.
Anisah Anisah, Budi Heru Santosa, and Dionysius Bryan Sencaki IEEE
Flood Risk Management (FRM) is implemented by the government to cope with floods, with mitigation/prevention, preparedness, response, and recovery phases. The occurrence of urban flooding regularly indicates that the applied FRM has not functioned effectively. This study proposes using systems thinking in flood risk management since the interdependence between flood risk components and the programs in the FRM is complex. For this reason, systems thinking in the form of a causal loop diagram can be used to explore the interdependence between programs within the FRM framework and flood risk components. By identifying the pattern of interdependence between flood risk and FRM, FRM programs can be directed to achieve the final target, namely reducing flood risk in an area. Therefore, life in flood-prone areas can occur sustainably and harmoniously.
Budi Heru Santosa, Dwi Nowo Martono, Rachmadhi Purwana, and Raldi Hendro Koestoer Insight Society
Awal Subandar, Budi Heru Santosa, Merry Effriana, Lena Sumargana, Nugroho, and Anisah Anisah IEEE
Lena Sumargana, Swasetyo Yulianto, Bangun Muljo Sukojo, Heri Sadmono, Bambang Winarno, Widyo Pura Buana, Fauziah Alhasanah, and Budi Heru Santosa IEEE
The Area Frame Sample for maize has been built to determine the random distribution of observation sites in all sub-districts in Indonesia. The principle and workings of the area frame sample for the rice crop were applied to the observation of maize plants. The difference is in the size of the segment being built, which is 100 x 100 meters in size and there are 4 units of observation points. The surveyor observes the maize growth stage with an application on the mobile phone then sends the results of the observations to a central server for processing. The growing phases and field conditions observed were Early Vegetative, Late Vegetative, Early Generative, Late Generative, Early Harvested, Young Harvested, Shilled Maize harvested, Land Preparation, no maize plant, non agriculture area and damage area or puso. This article describes the proposed Sampling Frame of Square Segment by Points for Maize Observation which carried out in Garut Regency, West Java, Indonesia.
Swasetyo Yulianto, Anisah Anisah, Agustan Agustan, Lena Sumargana, Yudi Anantasena, and Budi Heru Santosa IEEE
Budi Heru Santosa and Raldi Hendro Koestoer IEEE
Urban public green spaces (PGS), with their ecological, health support, and social functions needed by the community, would grow in terms of absolute number and spatial distributed populations. However, PGS's volume and spatial areas did not expand linearly to population growth; even in some cases, there has even been a decrease in volume and area for PGS. This paper examines PGS's planning process and management, which faces problems due to the population growth that requires more settlement areas and other socioeconomic facilities. The methodology applied was a comparative study in planning and managing PGS for two cases, Munich and the Yogyakarta municipal areas, which regionally have similar characteristics. The result shows that both regions tend to have proper governance for PGS. Also, both regions tend to have similar urban spatial structures associated with distributed growth centers of the polycentric system and address similar problems related to population growth. Despite the facts, both have differences in community perception of livable city function, especially for community cultural and social-relational aspects. In conclusion, this paper has highlighted that PGS's comprehensive planning is indispensable to achieve ecological, health support, and social functions to attain a livable city; therefore, a dynamic spatial model that considers variables: housing demand, urban spatial structure, urban growth form, and also community participation, could be a useful detecting tool to measure the level of development.