Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

By Farnaz Fotovatikhah, Manuel Herrera, Shahaboddin Shamshirband, Kwok-Wing Chau, Sina Ardabili, Md. Jalil Piran. 2018

327 citations recorded by the source corpus.

ABSTRACT Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people’s health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction.

Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/57ac9dfecab41c46564a121af9c766723ca2fbe6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kwok-Wing Chau
2. Pınar Öztürk
3. Amir Mosavi

In Environmental Engineering

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Ozgur Kisi
5. Hossein Bonakdari
6. Isa Ebtehaj
7. U. Aksoy
8. B. B. Ekici

In Global and Planetary Change

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Ozgur Kisi
5. U. Aksoy
6. B. B. Ekici

Citations in the corpus

• Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “References [77,78] provided reviews on ANN applications in flood.”
• COVID-19 Outbreak Prediction with Machine Learning (2020, unknown): “In other words, ML is the scientific study of algorithms and statistical models used by computer systems that use patterns and inference to perform tasks instead of using explicit instructions [74,75].”