a b s t r a c tThe development of flood early warning technology has grown rapidly. The technology has led to an increase in technology in terms of communication and information. Internet of Things technology (IoTs) has provided a major influence on the development of early warning information system. In this article a protipe-based flood monitoring information system of Google Maps have been designed by integrating Ultrasonic sensors as the height of the detector, the Arduino Uno as a processor, U-Blox GPS modules Neo 6 m GSM module and as the sender of data is the height of the water and the coordinates to the station of the system informais flood. The design of the prototype produces information flood elevations along with location based Google Maps interface. a b s t r a k Pengembangan teknologi peringatan dini banjir telah tumbuh dengan cepat. Teknologi tersebut telah mengarah kepada peningkatan di segi teknologi komunikasi dan informasi. Teknologi Internet of Things (IoTs) telah memberikan pengaruh besar terhadap perkembangan sistem informasi peringatan dini. Didalam artikel ini sebuah protipe sistem informasi monitoring banjir berbasis Google Maps telah dirancang dengan mengintegrasikan sensor ultrasonik sebagai pendeteksi ketinggian, Arduino Uno sebagai pemroses, modul GPS U-Blox Neo 6m dan modul GSM sebagai pengirim data ketinggian air dan koordinat ke stasion sistem informais banjir. Perancangan prototipe menghasilkan informasi ketinggian banjir beserta lokasinya berbasis antarmuka Google Maps.
Purpose-The purpose of this paper is to develop prototype of the information system of the flood monitoring based internet of things (IoT). This prototype serves to assist users in accessing flood levels through water levels and rainy weather conditions. Design/Methodology/Approach-This paper presents the design of information system of flood monitoring based internet of things (IoT). This prototype study acquires water level and rainfall data using ultrasonic sensors HC-SR04 and rain sensor. Data of flood height and rain levels detected by sensors are processed using Arduino Uno Microcontroller to produce output data in HTML format. Flood altitude information system and rainy weather from the microcontroller are distributed using ethernet module as web server integrated with Wireless N Router TL-MR3020 as a gateway path to the user. Findings-This research produces a prototype of web-based flood monitoring information system that has been able to distribute data of flood height and rainy weather in real time. Research Limitations/Implications-In the implementation of measurement, the information system only accesses one flood detector or one flooded location. Practical Implications-This research produces a prototype of web-based flood monitoring information system that has been able to distribute data of flood height and rainy weather in real time.
Flooding is a national disaster that often occurs in Indonesia. Flood disasters require long-term and short-term action. In the short-term system, the government currently emphasizes state and private institutions to jointly reduce flood victims by developing a flood disaster early warning system. Therefore, this study discusses the making of flood early warning information systems by utilizing GSM communication systems as a means of communication between clients and servers. The GSM communication service used is the SMS Gateway. The SMS gateway service is used for the first time sending data from a flood detection system to a flood information system. Second, disseminating flood information to the public. In this study, the flood warning system for flood early warning works with the integration of three modes.The three systems are flood detection systems, flood alarm systems, and flood early warning information systems. Flood detection systems are built using ultrasonic sensors and rain sensors as inputs, Arduino Uno as data processors and GSM SIM900 modules as outputs. The alarm system consists of GSM SIM900 module as Input, Arduino Uno as processor and electric alarm as output. The flood early warning information system was built using a Wavecom GSM modem, and data processing using PHP, MySQL DBMS, and Gammu. The communication system between each system uses SMS data. This method as a whole began in a flood detection system that sends flood and rain data to the flood early warning information system. And the flood warning system sends alarm activation data to the alarm system. Finally, the system distributes flood information to the public via SMS Gateway. This research is expected to help the community in anticipating more victims with flood information previously obtained
Synthetic Aperture Radar (SAR) is a potential application of remote sensing to geological and hydrometeorological hazards. This paper presents sustainability strategies for smart cities: the use of SAR Sentinel-1 for monitoring flood inundation and landslide hazards in Aceh Province, Indonesia. In this study, for flood detection, we attempt to uses Sentinel-1A (S-1A) in the same direction and acquisition through polarization of Vertical transmit and Vertical received (VV) – Vertical transmit and Horizontal received (VH) with a temporal baseline of 6 days. Those data were then analysed using the SNAP Toolbox. The results showed that the S-1A was successfully for detecting a flood inundation in which VH polarization is more sensitive than VV. For landslide monitoring, we apply multitemporal SAR images, where one of them is the Quasi-Persistent Scatterers (Q-PS) technique. Using ascending and descending orbit pass results in a better velocity map where both sides of the slope are detected due to the different sensor angle of both orbital passes. This technique has resulted in the undulating areas being monitored well and this will also fill the gap of layover and shadowing phenomena of the slant range SAR image. The Q-PS combinations were very effective to identify the deformation features associated with the land movement. For a smart city, natural hazards such as landslides and floods require continuous monitoring to ensure the potential as well as management and mitigation of natural hazards. Therefore, the InSAR technique is one of the effective ways for doing this strategy.
Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).
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