Real-Time River Level Monitoring Using GPS Heighting

GPS Solutions ◽  
2000 ◽  
Vol 4 (2) ◽  
pp. 63-67 ◽  
Author(s):  
Terry Moore ◽  
Kefei Zhang ◽  
Gareth Close ◽  
Roger Moore
2013 ◽  
Vol 347-350 ◽  
pp. 772-776
Author(s):  
Dong Jie Tan ◽  
Bing Han ◽  
Bao Yan Wang ◽  
Liang Liang Li

In order to overcome the disadvantage of low accuracy for traditional monitoring technologies of river level, and solve the problems in real-time data acquisition and remote transmission for the existing hydrological observation, we design a new monitoring system of river level based on fiber grating technology, which can realize high sensitivity and automatic monitoring of river level. The system has been put into practical application in a monitoring project and the measurement results indicate that this system is of such advantages as real-time capability, high accuracy, unattended operation, and can well meet the present requirements of hydrological observation and measurement.


Author(s):  
Krum Videnov ◽  
Vanya Stoykova

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.


2020 ◽  
Vol 21 (1) ◽  
pp. 56-67
Author(s):  
Husneni Mukhtar ◽  
Doan Perdana ◽  
Parman Sukarno ◽  
Asep Mulyana

ABSTRACTThe needs of flood disaster management encourage various efforts from all scientific disciplines of science, technology, and society. This article discusses the efforts to prevent flooding due to the habit of disposing of their waste into rivers through an innovative waste management system using the approach and application of Internet-based technology (IoT). Previous research has produced a prototype of the waste level monitoring system. In this research, the prototype was developed into a practical technology, called SiKaSiT (IoT Based Trash Capacity Monitoring System). This technology aims to assist janitor in monitoring, controlling and obtaining information about trash capacity and disposal time easily through an application on the smartphone in real-time and online. The system was made using a level detection sensor integrated with NodeMCU and Wi-Fi, MQTTbroker-protocol and Android-based application. Furthermore, the system was implemented in Bojongsoang adjacent to the Citarum river, where the water often overflowed due to the high rainfall and volume of trash around it. The results of system testing in the field shown good performance with value ranges of reliability is (99,785 - 99,944)% and availability is (99,786 - 99,945)%. SiKaSiT has several advantages over other similar systems. First, there is an application on the user's smartphone to monitor the capacity of trash and notification for full-bin. Second, the ability to operate on a small-bandwidth internet network because the throughput time is only around 0.59 kbps, thereby saving internet bandwidth consumption. This system has also helped overcome the problem of community trash management in Kampung Cijagra, where 60% of them gave feedback "agree" and the rest "strongly agree".Keywords: waste, IoT, monitoring, flooding, riverABSTRAKKebutuhan penanggulangan bencana banjir mendorong berbagai upaya dari semua disiplin ilmu baik dari bidang sains, teknologi dan sosial. Dalam artikel ini, penulis membahas upaya pencegahan banjir akibat kebiasaan membuang sampah ke sungai melalui inovasi sistem manajemen sampah menggunakan pendekatan dan penerapan teknologi berbasis Internet of Things (IoT). Pada riset sebelumnya telah dihasilkan sebuah prototype sistem monitoring level sampah. Kemudian pada riset ini prototype tersebut dikembangkan menjadi suatu teknologi tepat guna, dinamakan dengan SiKaSiT (Sistem Pemantauan Kapasitas Sampah Berbasis IoT). Teknologi ini bertujuan untuk membantu petugas kebersihan dalam memantau, mengontrol dan memperoleh informasi tentang kapasitas sampah dan waktu pembuangan sampah dengan mudah melalui aplikasi di smartphone secara real time dan online. Sistem dibuat dengan menggunakan sensor deteksi ketinggian sampah yang diintegrasikan dengan NodeMCU dan Wi-Fi, protokol MQTT broker dan aplikasi berbasis android pada smartphone. Selanjutnya sistem diimplementasikan di daerah Bojongsoang yang berdekatan dengan sungai Citarum yang airnya sering meluap akibat tingginya curah hujan dan volume sampah di sekitarnya. Hasil pengujian sistem di lapangan menunjukkan kinerja yang baik dengan kisaran nilai reliability adalah (99,785 – 99,944) % dan availability adalah (99,786 – 99,945) %. SiKaSiT memiliki beberapa kelebihan dibanding sistem serupa lainnya. Pertama, adanya aplikasi di smartphone pengguna untuk memonitor kapasitas sampah dan notifikasi saat tempat sampah penuh. Kedua, sistem mampu beroperasi pada jaringan internet bandwith kecil karena waktu throughput-nya hanya sekitar 0,59 kbps sehingga menghemat konsumsi bandwith internet. Sistem ini juga telah membantu menanggulangi permasalahan pengelolaan sampah masyarakat Kampung Cijagra, dimana 60% masyarakat memberi feedback “setuju” dan sisanya “sangat setuju”.Kata kunci: Sampah, IoT, Monitoring, Banjir, Sungai


Author(s):  
M. Consales ◽  
S. Principe ◽  
A. Iele ◽  
M. Leone ◽  
H. Zaraket ◽  
...  

1987 ◽  
Vol 5 (7) ◽  
pp. 1027-1033 ◽  
Author(s):  
T. Hirschfeld ◽  
F. Miller ◽  
S. Thomas ◽  
H. Miller ◽  
F. Milanovich ◽  
...  

2021 ◽  
Vol 25 (8) ◽  
pp. 4435-4453
Author(s):  
Remy Vandaele ◽  
Sarah L. Dance ◽  
Varun Ojha

Abstract. River-level estimation is a critical task required for the understanding of flood events and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river-camera images to estimate river levels but, currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We have developed an approach using an automated water semantic segmentation method to ease the process of river-level estimation from river-camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the rivers Severn and Avon, UK (21 November–5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91 %. Then, we apply our approach to year-long image series from the same cameras observing the rivers Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's correlation coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river-camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations.


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