Development of Disaster Prevention Support System for Irrigation Pond (DPSIP)

Hori, Toshikazu; Izumi, Akira; Shoda, Daisuke; Shigeoka, Tetsushi; Yoshisako, Hiroshi

doi:10.20965/jdr.2019.p0303

Cited by 5 publications

(3 citation statements)

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“…Several analysis systems have been constructed considering both f lood analysis and f lood control to determine the risk to downstream areas posed by an irrigation pond break. Studies include those by Tani (2005), Kikusawa (2007), Hori et al (2015b), and Hori et al (2019), the latter two of which are discussed in detail in this section.…”

Section: Computer Systems For Predicting the Risk Due To Irrigation Pmentioning

confidence: 99%

“…Therefore, various ponds still need to be ver if ied, in order to increase the accu racy and applicability of the proposed system. Hori et al (2019) developed the Disaster Prevention Support System for Irrigation Ponds (DPSIP) with the objective of enabling the sharing of pond break or status information in case of a major disaster. The objective of the system is to support early evacuation attempts by i m me d iat ely i n for m i ng t he pa r t ie s conce r ned downstream of a predicted failure upstream.…”

Section: Computer Systems For Predicting the Risk Due To Irrigation Pmentioning

confidence: 99%

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Review of Research on Irrigation Pond Damage due to Heavy Rain and Related Disaster Prevention Countermeasures in Japan

SangYoon

Kojima

Takemura

2020

JARQ

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There are about 170,000 irrigation ponds in Japan (May 2019) that serve a vital role as agricultural water resources in rural areas. However, these ponds are aging and the number of abandoned irrigation ponds has increased due to more farmland being abandoned and the aging of farmers. Many damaged irrigation ponds have been reported after heavy rains and earthquakes. During the past 10 years (2008-2017), 73% of all damaged irrigation ponds were damaged by heavy rainfall. About 64,000 ponds are specified as disaster-prevention-focused irrigation ponds (May 2019), which could pose a significant risk to downstream assets in case of a break. Thus, there has been rapidly growing social concern regarding disaster prevention and mitigation of the threat of such irrigation pond breaks. The prevention of irrigation pond failure due to heavy rain has consequently become a critical research topic. Accordingly, in this paper we review previous research on the damage to irrigation ponds due to heavy rain and the effects of various disaster prevention countermeasures relative to the following: 1) forms of damage and specific causes due to heavy rain; 2) flood analysis in case of a pond break, and flood control measures; and 3) methods of predicting potential damage due to an irrigation pond break.

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Section: Computer Systems For Predicting the Risk Due To Irrigation Pmentioning

confidence: 99%

Section: Computer Systems For Predicting the Risk Due To Irrigation Pmentioning

confidence: 99%

Review of Research on Irrigation Pond Damage due to Heavy Rain and Related Disaster Prevention Countermeasures in Japan

SangYoon

Kojima

Takemura

2020

JARQ

View full text Add to dashboard Cite

show abstract

“…Additionally, a cartographic representation of the areas most likely to be affected was created using 5000 simulations of synthetic scenarios and stochastic analysis (Monte Carlo approach). Hori et al [10] developed a disaster prevention support system for irrigation ponds in Japan after the failure of many off-stream reservoirs due to an earthquake in 2011. The system allows for determining the risk in heavy rain scenarios and calculating inundation areas in case of failure.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure

Silva-Cancino

Salazar

Sanz-Ramos

et al. 2022

Water

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Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy.

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Low-cost “buffer” structural measure for flooding risk reduction in irrigation reservoirs

Espejo,

Molina,

Zazo

et al. 2024

Journal of Hydrology

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Development of Disaster Prevention Support System for Irrigation Pond (DPSIP)

Cited by 5 publications

References 1 publication

Review of Research on Irrigation Pond Damage due to Heavy Rain and Related Disaster Prevention Countermeasures in Japan

Review of Research on Irrigation Pond Damage due to Heavy Rain and Related Disaster Prevention Countermeasures in Japan

A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure

Low-cost “buffer” structural measure for flooding risk reduction in irrigation reservoirs

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