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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Shahrum Shah Abdullah
Universiti Teknologi Malaysia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Flood Warning and Monitoring System Utilizing Internet of Things Technology Mohd Sabre, Mohamad Syafiq; Abdullah, Shahrum Shah; Faruq, Amrul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 4, November 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.948 KB) | DOI: 10.22219/kinetik.v4i4.898

Abstract

Flooding is one of the major disasters occurring in various parts of the world including Malaysia. To reduce the effect of the disaster, a flood warning and monitoring are needed to give an early warning to the victims at certain place with high prone to flood. By implementing Internet of Thing technology into the system, it could help the victim to get an accurate status of flood in real-time condition. This paper is develop a real-time flood monitoring and early warning system using wireless sensor node at a high prone area of flood. This system is based on NodeMCU based technology integrated using Blynk application. The wireless sensor node can help the victims by detecting the water levels and rain intensity while giving an early warning when a flood or heavy rain occurs. Basically, the sensor node consists of ultrasonic sensor and rain sensor controlled by NodeMCU as the microcontroller of the system which placed at the identified flood area. Buzzer and LED started to trigger and alert the victim when the flood had reached certain level of hazard. Data detected from the sensors are sent to the Blynk application via wireless connection. Victim will get to know the current status of flood and rain by viewing the interface and receiving push notification that available in Blynk application via IOS or Android smartphones. The flood level’s data sent to the email could help various organizations for further improvement of the system and flood forecasting purposes. After a test had been conducted, it was found that this prototype can monitor, detect and give warning with notification to the victim earlier before the occurrence of floods.
Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting Faruq, Amrul; Marto, Aminaton; Izzaty, Nadia Karima; Kuye, Abidemi Tolulope; Mohd Hussein, Shamsul Faisal; Abdullah, Shahrum Shah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 1, February 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i1.1156

Abstract

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.
Co-Authors Aminaton Marto Amrul Faruq Izzaty, Nadia Karima Kuye, Abidemi Tolulope MOHD HUSSEIN, SHAMSUL FAISAL Mohd Sabre, Mohamad Syafiq Muhamad Fadli Ghani