Around 12.62% of watersheds in Indonesia are damaged and need restoration immediately to prevent floods, landslides, and other related disasters. This study aimed to evaluate the Wosi watershed and formulate conservation scenarios to improve its conditions. The data collection included hydrology (quantity, continuity, and water quality), land (critical land index, percentage of vegetation cover, and erosion index), socio-economic (population pressure, welfare level, existence, and regulations enforcement), building investment (city classification and water building value classification), and the use of space (protected areas and cultivation areas). The evaluation of carrying capacity used a scoring analysis. The scenarios formulation used the information on potential carrying capacity and conservation strategies. The results suggested that the carrying capacity of the Wosi watershed from 2016-2019 had fallen into "bad" and "very bad" categories. To improve these conditions, the government can assign the riparian areas as a government asset, build ponds and dikes in the flood-prone areas, apply small recharge pond (SRP) on the cacao plantations, and assign Wosi Rendani protected forest (HLWR) as an urban forest. The implementation of these conservation strategies will result in the improvement of (a) the carrying capacity of the Wosi watershed and (b) the status into the "good" category to support environmentally friendly development in Manokwari city.
Flood is number one Indonesian natural disaster in the last 10 years and its occurrence at Manokwari is frequently reported. Biophysical condition is playing a key role in carrying capacity of this catchment area.This study is to determine biophysical characteristics of Wosi Watershed to manage and mitigate flooding in Manokwari. Spatial analysis and field observation methods were used to collect the data. Biophysical variables are rainfall, watershed morphometric, slope, and land used. Carrying capacity is measured using flow regime coefficient and annual flow coefficient. The results showed that the heavy rainfall (> 100 mm) throughout the ten years with 10.5 wet months at average resulting very wet tropical climate. This watershed has an area of 2,346.32 ha, its circumference of 29.95 km2 with river length of 8.38 km resulting 0.33 (triangle) and 1.027 (triangle) for Rc and Re, respectively. This morphometry is rectangular and slightly oval(triangular) formed of four rivers with drainage pattern of dendritic, which resembles the shape of a tree branch/twig. Steep slopes are dominant (58.5%), with non-forest area (62%) of the flat and steep slope for settlement (698 ha), and flat slope for mixed dry farming (707 ha). From 2016-2020, river water flow changes rapidly from low to very high to generate flooding, but the carrying capacity is sometime changeable from good to bad. Water drainage, retaining walls, replantation, early warning system, and flooding leaflets mitigation campaign, are structural and non-structural mitigation could be parallelly conducted to manage and mitigate the flooding risks in future.
The 2019 corona virus (Covid-19) pandemic is a global problem for now. One way to deal with the spread of the corona virus is to maintain a distance of at least one meter and stay away from crowds. Therefore, a crowd detection warning system based on a deep convolutional neural network (deep CNN) was developed using CCTV. The development of this system was carried out using the NVIDIA Jetson Nano microcontroller as the computing hardware. Crowd object detection uses the OpenCV library, the YOLOv3-Tiny algorithm, and the euclidean distance method to calculate the distance between 'person' objects. Based on the tests carried out on function and performance, the results obtained that this crowd detection warning system can detect 'person' objects with an accuracy rate of 92.79. In addition, this system has also been able to detect several types of colors from objects so that warning messages can be given more specifically on the color of the clothes of the 'person' in the detected crowd.
Daerah aliran sungai (DAS) Arui merupakan salah satu DAS di Kabupaten Manokwari yang masuk dalam klasifikasi dipulihkan. Hal tersebut dikarenakan DAS Arui mengalami dampak kejadian banjir limpasan setiap hujan dengan intensitas yang tinggi. Kajian tentang kerawanan, variabel geomorfologi yang berpengaruh terhadap banjir limpasan dan tindakan mitigasi yang tepat diperlukan untuk pengendalian banjir limpasan tersebut. Penelitian ini bertujuan untuk mengidentifikasi tentang bahaya kerawanan banjir limpasan di DAS Arui, mengetahui faktor-faktor bio-fisik atau geomorfologi yang mempengaruhi kerawanan banjir limpasan, serta merekomendasikan mitigasi banjir limpasan. Penelitian ini dirancang dengan metode deskriptif kuantitatif, dimana data digital dari variabel penelitian di kuantifikasi dengan skor dan bobot untuk mendapatkan skor total. Data digital peta diolah dengan menggunakan Arc. GIS dan disajikan dalam bentuk tabel dan peta. Hasil penelitian menunjukkan wilayah DAS Arui memiliki potensi kerawanan banjir limpasan tinggi hingga sangat tinggi seluas 12.371,71 ha (55,89%), variabel geomorfologi yang dominan berpengaruh terhadap banjir limpasan sebesar 55,89% adalah kelerengan. Kegiatan normalisasi Sungai Nimboy telah dilakukan sebagai upaya mitigasi struktural, dan non struktural lebih menekankan kepada partisipasi aktif masyarakat setempat.
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