National Flood Forecasting and Warning System of Malaysia: An Overview

Azad, W. H.; Hassan, M. M. A.; Ghazali, Nor Hisham M.; Weisgerber, A.; Ahmad, Fuad Febrian

doi:10.1007/978-981-15-1971-0_27

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…This is especially true during the monsoon season. Several government organizations, including the Department of Irrigation and Drainage (DID) in Malaysia, the Malaysian Meteorological Department (MMD), and local governments are in charge of managing floods, but there have been instances when the efforts of these organizations have not been effectively coordinated, resulting in ineffective and inefficient flood management [61,62]. Slow action from the Federal Government, especially the National Disaster Management Agency (NADMA), coupled with the chaotic coordination of the local government and local agencies has apparently complicated this crisis for the people and made it a governance disaster [31,63].…”

Section: Inadequate Coordination and Communicationmentioning

confidence: 99%

Reviewing Challenges of Flood Risk Management in Malaysia

Rosmadi,

Ahmed,

Mokhtar

et al. 2023

Water

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This study reviewed some of the challenges faced by local authorities in disaster management, especially flood disasters that occurred in Malaysia. Flood disasters are the most frequent disasters in Malaysia, especially during the monsoon seasons. The hard structure developed by Malaysia’s National Security Council (MKN) under ‘Directive 20’ is used to manage disasters in the country. Although Malaysia has become more skillful in managing flood disasters, the frequent climate changes along with weakness in implementing flood risk management plans resulted in much losses and damages throughout the country. Therefore, this study explored the gaps and weaknesses in flood risk management (FRM) in Malaysia by reviewing the available literature to recommend better flood management. This study revealed four main issues which are weaknesses in terms of (i) coordination and communication, (ii) manpower and assets, (iii) public awareness, and (iv) power and authority among local authorities to implement flood management plans. The capacity of local authorities and individuals in charge of disaster management is inadequate, especially for flood risk preparedness and management. Hence, responsible individuals are also in a vulnerable situation to implement management plans or rescue operations when flood disasters occur since they are also flood victims. Thus, the National Disaster Management Agency (NADMA) which acts as the main coordinator of disaster management in Malaysia should re-examine the flood management plan to ensure that it can be implemented efficiently and effectively, especially at the local level as they are the first respondents on the scene when the disaster occurs. The combination of both structural and non-structural measures might require in many cases the management of flood disasters; however, the disaster risk preparedness and management of individuals via customized training is a must to prevent flood disasters as well as minimize their impact. The flood management plan should also incorporate natural-based approaches at the whole-river-basin level for the long-term solution and sustainable development, not only focusing to manage the localized flood problem at the specific area.

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Section: Inadequate Coordination and Communicationmentioning

confidence: 99%

Reviewing Challenges of Flood Risk Management in Malaysia

Rosmadi,

Ahmed,

Mokhtar

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…In connection with disaster risk management and flood warning system, according to DID [19] and Sulaiman [20] for river water level data-above sea level, it can be classified into three main categories, including "alert level", "warning level", and "danger level". The river water level at Guillemard bridge, located in the Kelantan River, was selected as a flood forecasting point in the first dataset.…”

Section: Flood Early Warning Systemmentioning

confidence: 99%

Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems

Faruq

Hussein

Marto

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Flood forecasting is crucial for early warning system and disaster risk reduction. Yet the flood river water levels are difficult and challenging task that it cannot be easily captured with classical time-series approaches. This study proposed a novel intelligence system utilised various machine learning techniques as individual models, including radial basis function neural network (RBF-NN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and long short-term memory network (LSTM) to establish intelligent committee machine learning flood forecasting (ICML-FF) framework. The combination of these individual models achieved through simple averaging method, and further optimised using weighted averaging by K-nearest neighbour (K-NN) and genetic algorithm (GA). The effectiveness of the proposed model was evaluated using real case study for Malaysia’s Kelantan River. The results show that ANFIS outperforms as individual model, while ICML-FF-based model produced better accuracy and lowest error than any one of the individuals. In general, it is found that the proposed ICML-FF is capable of robust forecasting model for flood early warning systems.

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“…In connection with disaster risk management and flood warning system, according to DID [31] and [32] for river water level data-above sea level, it can be classified as three main categories, including "alert level", "warning level", and "danger level". The alert level for Kelantan River, in Kelantan is 12.50 meters, while for warning level is 15.10 meters, and danger level is 18.60 metersabove sea levels [33].…”

Section: Flood Disaster and Early Warningmentioning

confidence: 99%

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

Faruq

Marto

Izzaty³

et al. 2021

KINETIK

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Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.

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National Flood Forecasting and Warning System of Malaysia: An Overview

Cited by 7 publications

References 2 publications

Reviewing Challenges of Flood Risk Management in Malaysia

Reviewing Challenges of Flood Risk Management in Malaysia

Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

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