Forecasting Water Level Of Jhelum River Of Kashmir Valley India, Using Prediction And Earlywarning System

The hydrological disasters have the largest share in global disaster list and in 2016 the Asia’s share was 41% of the global occurrence of flood disasters. The Jammu and Kashmir is one of the most flood-prone regions of the Indian Himalayas. In the 2014 floods, approximately 268 people died and 168004 houses were damaged. Pulwama, Srinagar, and Bandipora districts were severely affected with 102, 100 and 148 km 2 respectively submerged in floods. To predict and warn people before the actual event occur, the Early Warning Systems were developed. The Early Warning Systems (EWS) improve the preparedness of community towards the disaster. The EWS does not help to prevent floods but it helps to reduce the loss of life and property largely. A flood monitoring and EWS is proposed in this research work. This system is composed of base stations and a control center. The base station comprises of sensing module and processing module, which makes a localised prediction of water level and transmits predicted results and measured data to the control center. The control center uses a hybrid system of Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the supervised machine learning technique, Linear Multiple Regression (LMR) model for water level prediction. This hybrid system presented the high accuracy of 93.53% for daily predictions and 99.91% for hourly predictions.

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Water level detector for early warning systems using color difference measurement

10.1063/1.5043006 ◽

2018 ◽

Author(s):

Fendi Aji Purnomo ◽

Nanang Maulana Yoeseph ◽

Berliana Kusuma Riasti ◽

Ryan Wahyu Anggara

Keyword(s):

Water Level ◽

Early Warning ◽

Color Difference ◽

Early Warning Systems ◽

Warning Systems ◽

Difference Measurement ◽

Level Detector

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Methodology for Simulation of IoT-Based Flood Data Collection Infrastructure

electronic Journal of Computer Science and Information Technology ◽

10.52650/ejcsit.v7i1.113 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Azimah Abdul Ghapar ◽

Salman Yussof

Keyword(s):

Data Collection ◽

Internet Of Things ◽

Early Warning ◽

Real World ◽

Research Work ◽

Early Warning Systems ◽

Warning Systems ◽

Flood Prediction ◽

Software Simulation ◽

Related Data

Internet of Things (IoT) is a potential technology to be used for data collection tasks in real-world environments. However, due to the difficulty of deploying and testing a real IoT implementation, many researchers end up having to use software simulation to evaluate their proposed techniques. This paper focuses on the use of IoT for collecting flood-related data, which would then use by flood-related applications such as flood prediction applications and flood early warning systems. This paper proposed a methodology for simulating the IoT system used for flood data collection. The proposed methodology consists of four main steps which are identifying the flood environment, defining the architecture for flood data collection, simulating the IoT-based flood data collection infrastructure and analyzing the results. The activities for each step are described in detail as to guide other researchers in the same area to adapt the methodology to their research work.

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Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS

Remote Sensing ◽

10.3390/rs13244977 ◽

2021 ◽

Vol 13 (24) ◽

pp. 4977

Author(s):

Shuangshuang Wu ◽

Xinli Hu ◽

Wenbo Zheng ◽

Matteo Berti ◽

Zhitian Qiao ◽

...

Keyword(s):

Water Level ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Early Warning Systems ◽

Water Levels ◽

Deformation Model ◽

Warning Systems ◽

Velocity Threshold ◽

Landslide Deformation

The triggering threshold is one of the most important parameters for landslide early warning systems (EWSs) at the slope scale. In the present work, a velocity threshold is recommended for an early warning system of the Gapa landslide in Southwest China, which was reactivated by the impoundment of a large reservoir behind Jinping’s first dam. Based on GNSS monitoring data over the last five years, the velocity threshold is defined by a novel method, which is implemented by the forward and reverse double moving average of time series. As the landslide deformation is strongly related to the fluctuations in reservoir water levels, a crucial water level is also defined to reduce false warnings from the velocity threshold alone. In recognition of the importance of geological evolution, the evolution process of the Gapa landslide from topping to sliding is described in this study to help to understand its behavior and predict its potential trends. Moreover, based on the improved Saito’s three-stage deformation model, the warning level is set as “attention level”, because the current deformation stage of the landslide is considered to be between the initial and constant stages. At present, the early warning system mainly consists of six surface displacement monitoring sites and one water level observation site. If the daily recorded velocity in each monitoring site exceeds 4 mm/d and, meanwhile, the water level is below 1820 m above sea level (asl), a warning of likely landslide deformation accelerations will be released by relevant monitoring sites. The thresholds are always discretely exceeded on about 3% of annual monitoring days, and they are most frequently exceeded in June (especially in mid-June). The thresholds provide an efficient and effective way for judging accelerations of this landslide and are verified by the current application. The work presented provides critical insights into the development of early warning systems for reservoir-induced large-scale landslides.

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The CARE Telematics Network for the Surveillance of Influenza in Europe

Methods of Information in Medicine ◽

10.1055/s-0038-1634617 ◽

1995 ◽

Vol 34 (05) ◽

pp. 518-522 ◽

Cited By ~ 13

Author(s):

M. Bensadon ◽

A. Strauss ◽

R. Snacken

Keyword(s):

Public Health ◽

Early Warning ◽

Early Warning Systems ◽

Warning Systems ◽

Surveillance Networks ◽

Spanish Flu ◽

The 1950S ◽

National Networks ◽

First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

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