Development of Internet of Thing (IoT) technology for flood prediction and Early Warning System (EWS)

Flood is the most significant disaster happened in almost every part of the world. When the event occurred, it causes great losses in economic and human life. Implementation of the advancement of ICT brings significant contribution to reduce the impact of flood toward the people and properties. This paper attempts to investigate the capability of internet of things (IoT) technology in reducing the impact of natural disaster specifically in flood disaster scenario. First, the concept of Internet of Things (IoT), key technologies and its architecture are discussed. Second, related research work on IoT in disaster context will be discussed. Third, further discussion on the propose Internet of Things (IoT) architecture and key components in the development of flood prediction and early warning system. The smart sensors will be placed at river basin for real-time data collection on flood related parameter such as rainfall, river flaw, water level, temperature, wind direction and so on. The data will be transmitted to data centre via wireless communication technology which will be processed and measured on the cloud service, then the alert information will be sent users via smart phone. Thus, early warning message is received by the people in terms of location, time and other parameters relate to flood

Evaluation of Machine Learning approach in flood prediction scenarios and its input parameters: A systematic review

Flood disaster is a major disaster that frequently happens globally, it brings serious impacts to lives, property, infrastructure and environment. To stop flooding seems to be difficult but to prevent from serious damages that caused by flood is possible. Thus, implementing flood prediction could help in flood preparation and possibly to reduce the impact of flooding. This study aims to evaluate the existing machine learning (ML) approaches for flood prediction as well as evaluate parameters used for predicting flood, the evaluation is based on the review of previous research articles. In order to achieve the aim, this study is in two-fold; the first part is to identify flood prediction approaches specifically using ML methods and the second part is to identify flood prediction parameters that have been used as input parameters for flood prediction model. The main contribution of this paper is to determine the most recent ML techniques in flood prediction and identify the notable parameters used as model input so that researchers and/or flood managers can refer to the prediction results as the guideline in considering ML method for early flood prediction

Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters

Flood catastrophes are among the natural disasters that have occurred regularly around the world. Malaysia is one of the countries that experience flood disasters on a yearly basis, most notably during the monsoon season, which runs from November to January. This study developed a novel flood forecasting model through the application of advanced machine learning (ML), deep learning (DL), and natural language processing (NLP) for sentiment analysis and text classification before a flood event, during a flood event and after a flood event. The morphometric ranking approach (MRA) was used to identify flood-susceptibility areas. Various data sources were collected including natural dimension such as rainfall intensity (mm), streamflow (cm/s), and water level (m) from Department of Irrigation and Drainage, and social dimension like text data extracted from Twitter platform. A digital elevation model (DEM) was used to process parameters for MRA with the application of geographic information system (GIS) for identifying flood-prone areas. General ML pipelines were used before building the model such as data pre-processing, data exploration to detect outliers, and filling missing values. The flood forecasting model used advanced machine learning and deep learning specifically long-short term memory (LSTM) which is suitable for time series data of rainfall and streamflow forecasting. Additionally, the model was developed using these three models: LSTM, ARIMA, and FB Prophet. The forecasting results indicated that the LSTM model has a root mean square error (RMSE) of 10.76, which is more accurate in comparison to the other models ARIMA and FB Prophet, which have RMSE values of 14.15, and 14.23, respectively. The accuracy of the model of text classification algorithm for predicting flood events is 0.87. Flood susceptibility mapping using MRA revealed that sub-catchments 5, 24, and 25 were highly susceptible to flooding. These sub-catchments were located in Jeli, Kuala Krai sub-district, and Gua Musang sub-district, respectively. In sum, this flood forecasting model is vital to provide flood information for early warning system to enable flood managers or decision-makers to make more informed plans during the flood preparation and mitigation phases, thereby minimizing the impact of floods on people, property, and the environment

Development of Internet of Thing (IoT) technology for flood prediction and Early Warning System (EWS)

Flood is the most significant disaster happened in almost every part of the world. When the event occurred, it causes great losses in economic and human life. Implementation of the advancement of ICT brings significant contribution to reduce the impact of flood toward the people and properties. This paper attempts to investigate the capability of internet of things (IoT) technology in reducing the impact of natural disaster specifically in flood disaster scenario. First, the concept of Internet of Things (IoT), key technologies and its architecture are discussed. Second, related research work on IoT in disaster context will be discussed. Third, further discussion on the propose Internet of Things (IoT) architecture and key components in the development of flood prediction and early warning system. The smart sensors will be placed at river basin for real-time data collection on flood related parameter such as rainfall, river flaw, water level, temperature, wind direction and so on. The data will be transmitted to data centre via wireless communication technology which will be processed and measured on the cloud service, then the alert information will be sent users via smart phone. Thus, early warning message is received by the people in terms of location, time and other parameters relate to flood