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  • Open Access


    Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

    G. Indra1,*, N. Duraipandian2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1455-1470, 2023, DOI:10.32604/iasc.2023.027703

    Abstract A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of… More >

  • Open Access


    Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

    Shidrokh Goudarzi1,2,*, Seyed Ahmad Soleymani2,3, Mohammad Hossein Anisi4, Domenico Ciuonzo5, Nazri Kama6, Salwani Abdullah1, Mohammad Abdollahi Azgomi2, Zenon Chaczko7, Azri Azmi6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 715-738, 2022, DOI:10.32604/cmc.2022.019550

    Abstract The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is… More >

  • Open Access


    Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

    Amrul Faruq1, Aminaton Marto2, Shahrum Shah Abdullah3,*

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 297-306, 2021, DOI:10.32604/csse.2021.017468

    Abstract The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were… More >

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