Measurement Design of Sensor Node for Landslide Disaster Early Warning System

Sofwan, Aghus; Sumardi, -; Irsyad, R Muhammad; Najib, Najib

doi:10.1109/icon-eei.2018.8784341

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…Later, this ANN intelligent system will be embedded into a developed hardware system shown in Figure 2. It consists of sensors, such as a reed switch, YI-69, MPU 6050, 801S, and DHT22 ( Sofwan et al, 2018a;Sofwan et al, 2018b). The sensors measure physical parameters, such as rainfall, slope, soil moisture, and vibration.…”

Section: Methodsmentioning

confidence: 99%

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Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Sofwan¹,

Sumardi²,

Najib³

et al. 2021

JST

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Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver these factor values into the levels of vulnerability of landslide disasters is needed. The system uses Arduino Mega 2560 to process the level of vulnerability. It can predict the moment and the probability of the disaster occurring as an early warning system. The artificial neural network (ANN) intelligent system can expect an event of a disaster. The designed ANN used five parameters causing landslide as input data: rainfall, slope, soil moisture on the surface, soil moisture in the ground’s depth, and soil vibration. The ANN system output delivered three-level conditions: the safe, the standby, and the hazardous. The feed-forward backpropagation (FFBP) and the cascade forward backpropagation (CFBP) methods were analyzed. The performance of both methods was compared in terms of minimum square error (MSE). The MSE results of FFBP and CFBP in the safe, the standby, and the hazardous conditions were 0.017076 and 0.034952; 0.049597 and 0.046764; 0.062105 and 0.060355; respectively. The results point to the supremacy of CFBP to FFBP in standby and hazardous conditions. Therefore, the CFBP is implemented into the hardware of the early warning system.

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Section: Methodsmentioning

confidence: 99%

“…Our research contribution is more focused on developing the ANN model with FFBP and CFBP methods. Whereas, Sofwan et al (2018a) published our hardware system development. The hardware is the node, which consists of sensors, microcontroller Arduino Mega 2560, communication module, and solar cell power supply.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Sofwan¹,

Sumardi²,

Najib³

et al. 2021

JST

Self Cite

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“…The test was performed by analyzing the difference of output value from neural network Generalized Regression and Feed-Forward Back Propagation Neural Network which have been designed. Then the results were compared to manual calculation using (5). Table II exposes data from the taken test.…”

Section: A Grnn Test On Obtained Field Datamentioning

confidence: 99%

“…The safe condition simulation was performed with various obtained values, which is shown in Table IV. Furthermore, the performance test was done by analyzing the difference of output value of artificial neural network Generalized Regression and Feed-Forward Backpropagation which has been designed with manual calculation using (5). The scores range that states the safe condition is from 1-1.69.…”

Section: B Safe Condition Simulationmentioning

confidence: 99%

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Early Warning System of Landslide Disaster using Generalized Neural Network Algorithm

Sofwan

Sumardi

Azka

2019

2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

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Landslides are frequently happened Indonesia, as many as 274 districts / cities are prone to landslides. There are many parameters that affect the landslide occurrence such as rainfall, land slope, soil moisture, and vibration. It is needed to provide a system that not only able to process data parameters to provide early warning of landslide disaster, but also increase the readiness of the population to minimize losses caused by this disaster. Generalized Regression Neural Network method is used to identify the effect of each parameter on the occurrence of landslide disaster. Tests conducted on field conditions and simulations on safe, alert, and danger condition to know the calculation result of artificial neural network. The simulation results are compared with the artificial neural network feed forward back propagation and manual calculations to demonstrate the effectiveness of the proposed method. The validation test on field condition using simulation shows average error of Generalized Regression method and Feed Forward Backpropagation method are 0.00115 and 0.08702, respectively. Furthermore, the Mean Square Error performance of the former method is better than that of the latter with values of 2.9157e-06 and 0.0112, severally.

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