Measuring the economic benefits of advanced technology use for river flood forecasting

In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum–Cunge scheme.

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Hybrid Systems for River Flood Forecasting Using MLP, SOM and Fuzzy Systems

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_58 ◽

2009 ◽

pp. 557-566 ◽

Cited By ~ 1

Author(s):

Ivna Valença ◽

Teresa Ludermir

Keyword(s):

Hybrid Systems ◽

Fuzzy Systems ◽

Flood Forecasting ◽

River Flood

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River-flood forecasting methods: the context of the Kelantan River in Malaysia

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/880/1/012021 ◽

2021 ◽

Vol 880 (1) ◽

pp. 012021

Author(s):

M Abdul Majid ◽

M Hafidz Omar ◽

M Salmi M Noorani ◽

F Abdul Razak

Keyword(s):

Natural Disasters ◽

Computer Modelling ◽

Flood Forecasting ◽

Weather Data ◽

Economic Losses ◽

Current State ◽

Forecasting Methods ◽

River Flood ◽

Future Direction ◽

State Of Affairs

Abstract River-flood forecasting is among the most important feasible non-structural approaches used in reducing economic losses and alleviating human sufferings. In spite of uncertainty in the forecasting of natural disasters, the current prevailing methods developed in many parts of the world in the recent history has made good progress to a great extent. The advancement is attributed mainly due to the availability of high-resolution weather data and the use of sophisticated computer modelling algorithms. However, it is desirable to conduct exploratory review studies to further improving the current state of affairs. The present paper reviews briefly the river-flood forecasting methods currently used worldwide with a specific focus in the context of the Kelantan River in Malaysia. Flooding in Malaysia is recurrent covering a large inhabited area compared with other natural disasters. Some of the popularly used methods in the literature such as statistical methods machine learning and methods based on chaos theory have been reviewed, The paper will also attempt to explore the future direction for research and development that might be useful specifically for dealing with the recurrent rivers flooding in Malaysia. A reasonably acceptable prediction of river streamflow is significantly important in disaster management and water resources management.

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