The August 1998 Flood Disaster in the Abukuma River Basin.

Mano, Akira; Takahashi, Masayuki; Imamura, Fumihiko; Nagabayashi, Hisao

doi:10.2328/jnds.22.1

Cited by 4 publications

(2 citation statements)

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“…The floods were caused by heavy rain in the 3 10 44 24 220 6 20 41 24 205 9 20 42 24 210 12 20 40 24 200 upstream accompanied landslide, drift wood and sedimentation. Monetary losses in Fukushima prefecture were estimated as 109 and 83 billion Yens in 1986 and 1998 floods, respectively (Mano et al, 2000). The Hybrid model was applied for forecasting of rainfall and water level at Koriyama City, Japan.…”

Section: Koriyama Flood Japanmentioning

confidence: 99%

Developing a hybrid multi‐model for peak flood forecasting

Chidthong

Tanaka

Supharatid

2009

Hydrological Processes

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Abstract:The present study aims to develop a hybrid multi-model using the soft computing approach. The model is a combination of a fuzzy logic, artificial neural network (ANN) and genetic algorithm (GA). While neural networks are low-level computational structures that perform well dealing with raw data, fuzzy logic deal with reasoning on a higher level by using linguistic information acquired from domain experts. However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment. Moreover, experts occasionally make mistakes and thus some rules used in a system may be false. A network type structure of the present hybrid model is a multi-layer feed-forward network, the main part is a fuzzy system based on the first-order Sugeno fuzzy model with a fuzzification and a defuzzification processes. The consequent parameters are determined by least square method. The back-propagation is applied to adjust weights of network. Then, the antecedent parameters of the membership function are updated accordingly by the gradient descent method. The GA was applied to select the fuzzy rule. The hybrid multi-model was used to forecast the flood level at Chiang Mai (under the big flood 2005) and the Koriyama flood (2003) in Japan. The forecasting results are evaluated using standard global goodness of fit statistic, efficient index (EI), the root mean square error (RMSE) and the peak flood error. Moreover, the results are compared to the results of a neuro-genetic model (NGO) and ANFIS model using the same input and output variables. It was found that the hybrid multi-model can be used successfully with an efficiency index (EI) more than 0Ð95 (for Chiang Mai flood up to 12 h ahead forecasting) and more than 0Ð90 (for Koriyama flood up to 8 h ahead forecasting). In general, all of three models can predict the water level with satisfactory results. However, the hybrid model gave the best flood peak estimation among the three models. Therefore, the use of fuzzy rule base, which is selected by GA in the hybrid multi-model helps to improve the accuracy of flood peak.

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Section: Koriyama Flood Japanmentioning

confidence: 99%

Developing a hybrid multi‐model for peak flood forecasting

Chidthong

Tanaka

Supharatid

2009

Hydrological Processes

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“…It is on this basis that researchers are trying to design a device that can help in natural disaster conditions where the electricity network will be cut off after a natural disaster [3]. This device is called a portable solar generator that can be used for emergencies [4]. This device can also be used for homes affected by power outages [5].…”

Section: Introductionmentioning

confidence: 99%

Designing a portable solar generator for emergencies

Widyartono,

Aribowo,

Rahmadian

et al. 2024

E3S Web Conf.

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Indonesia is a country made up of thousands of islands. Indonesia is located between four tectonic plates, the Indian Ocean Plate, and the Pacific Ocean Plate, the Asian Continental Plate, and the Australian Continental Plate. Geographically, the territory of Indonesia is in the series of Ring of Fire. The Ring of Fire, also called the Circum-Pacific Belt, is a 40,000 km-long chain of volcanoes and seismically active sites spanning the Pacific Ocean. This condition is very potential as well as prone to disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. The impact of this disaster can make it difficult to access clean water, electricity, and fuel oil networks. Access to electricity is not available because the power grid is cut. The purpose of this research is to be able to provide first aid in terms of electricity so that it can relieve victims of natural disasters or volunteers who work to evacuate victims. The output targeted in this research is a Portable Solar Generator that can be used in an emergency by disaster-affected victims or by volunteers. With a 35-watt load, it could operate for 5 hours 30 minutes and with 55 Watt load it can operate for 3 hours. In terms of recharging time, the charging process using a 50 Wp solar panel takes approximately 7 hours, this is because the charging process is carried out outside the building according to actual conditions.

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Analysis of vulnerability level to tsunami on the Bengkulu City coastal using Geographic Information System

Fathiyah,

Arhatin,

Siregar

2024

BIO Web Conf.

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Bengkulu City is one of the areas that has a high potential for experience earthquake and tsunami disasters because of its location right above the subduction zone of the Indo-Australian plate with the Eurasian plate. The objective of this study was to identify and map the degree of susceptibility to tsunamis in the coastal regions of Bengkulu City. The tsunami vulnerability map was created used the overlay method used five parameters, namely land elevation, land slope, distance from rivers, distance from the coastline, and land use. This research, which covers 14 sub-district areas was carried out used Geographic Information System (GIS). The level of tsunami vulnerability is divided into five classes, namely very safe with an area of 230.82 ha (1.19%), safe with an area of 4,402.30 ha (22.75%), moderately vulnerable with an area of 8,135.78 ha (42.04%), vulnerable with an area of 5,361.06 ha (27.70%), and very vulnerable with an area of 1,222.38 ha (6.32%). The city of Bengkulu is dominated by the medium vulnerability class so that in general it is classified as not safe enough from the risk of a tsunami disaster.

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The August 1998 Flood Disaster in the Abukuma River Basin.

Cited by 4 publications

References 0 publications

Developing a hybrid multi‐model for peak flood forecasting

Developing a hybrid multi‐model for peak flood forecasting

Designing a portable solar generator for emergencies

Analysis of vulnerability level to tsunami on the Bengkulu City coastal using Geographic Information System

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