Integrated flood analysis system (IFAS) for Kelantan river basin

Hafiz, I.; Sidek, Lariyah Mohd; Basri, Hidayah; Fukami, Kimio; Hanapi, Muhammad Nassir; Livia, L.; Jaafar, Aminah Shakirah

doi:10.1109/istt.2014.7238196

Cited by 23 publications

(7 citation statements)

References 1 publication

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“…A land use classification study for 20 years, from 1994 to 2014, revealed a 13.7 % decrease in forest land, and a 6.2 % increase in oil palm plantations [37] in the catchment. These spurred many hydrological and modeling studies to be conducted in the area [38,39]. Some modeling studies on the Kelantan catchment adopted semi-distributed approaches like HEC-HMS in modeling runoff using the Soil Conservation Service Curve Number (SCS-CN) method [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Umar¹,

Zulkafli²,

Yusuf³

et al. 2020

Preprint

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Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1 - considering the flexibility, modularity and portability - and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e. the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. 32 years' continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

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Section: Introductionmentioning

confidence: 99%

Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Umar¹,

Zulkafli²,

Yusuf³

et al. 2020

Preprint

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“…Recent research works who considered optimisation methods in creating reservoir release policies showed that modified PSO would be an ideal optimisation technique for creating reservoir release policy [6][7][8][9][10]. Some other related works on reservoir hydrology can be found here- [11][12][13][14][15][16][17][18][19]. This research is intended to include passive congregation theory by updating the key properties of the particle movement (Velocity update) inside the standard PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Passive Congregation Theory for Particle Swarm Optimization (PSO): an Application in Reservoir System Operation

Hossain¹,

Sidek²,

Marufuzzaman

et al. 2018

IJET

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Particle swarm optimisation (PSO) is a very well-known method and has a strong background in optimisation filed to solve different non-linear, complex problems especially in creating the reservoir release policies. This research modified the particle updating process of the standard PSO algorithm by including the passive congregation (PC) theory. The passive congregation theory of natural being’s social behaviour is adopted to updated the standard PSO algorithm and used to develop and optimise a reservoir release policy for monthly basis. The inflow data to the dam/reservoir has categorised into three different categories (High, medium and low). The problem is formulated on correspondence to the release and capacity constraints. Water deficit from the release is aimed to be minimised and formulated as the main objective function. Monthly releases are taken as the main objective variables and are essentially control the water deficit of the process. The standard form of PSO then compared with the updated version and the results is analysed by adopting different performance measuring indicators such as reliability, vulnerability and resilience. The results showed that the updated PSO-PC is more capable of the standard PSO (5% more reliable; 0.02 less vulnerable and 1.5 more resilience) in providing optimum results for a reservoir system.

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“…In fact, it becomes the most important natural disaster in term of the population affected, frequency, financial cost and the disruption of socio-economic activities. [1] Different states in Malaysia, the flood has become a common disaster because it happened almost every year. The northeast monsoon usually happened between November and February caused the heavy rainfall as much as 610mm in 24 hours in ultimate cases primarily in the east coast of Peninsular Malaysia and to Sabah and Sarawak.…”

Section: Introductionmentioning

confidence: 99%

Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

Snu

Sidek²,

et al. 2018

IJET

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The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

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Integrated flood analysis system (IFAS) for Kelantan river basin

Cited by 23 publications

References 1 publication

Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Passive Congregation Theory for Particle Swarm Optimization (PSO): an Application in Reservoir System Operation

Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

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