Forecasting Natural Disasters of Tornados Using mHGN

Nasution, Benny Benyamin; Sembiring, Rahmat Widia; Sundawa, Bakti Viyata; Gunawan,; Amelia, Afritha; Ismael,; Sunjaya, Handri; Alifuddin, Suhaili; Pardede, Morlan; Junaidi, Junaidi; Syahruddin, Muhammad; Lubis, Zulkifli

doi:10.1007/978-3-319-68486-4_13

Cited by 6 publications

(9 citation statements)

References 6 publications

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“…fires, sunken ships) or sudden and random occurrences of natural disasters [10]. Events of natural disasters such as volcanic eruptions and earthquakes do not occur within a certain period of time, people cannot make the best preparations to deal with them [19]. The incidents experienced in the patients mentioned above are treatments that must be dealt with quickly, which are called priority patients.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Health Care Services with Limited Resources

Pardede¹,

Mawengkang²,

Zarlis³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Health services are an integral part of hospitals or health clinics. Maximum service can get if the availability of resources in the service center is very adequate, but the availability of resources cannot be ascertained that it will always be adequate, and excessive availability of resources can also result in waste. The problem that often occurs is the lack of optimal services provided to patients due to limited available resources. Various obstacles, such as services that are not permitted are repeated, uncertain service distances, and service time are optimal barriers to service. This study aims to solve the problem of optimizing health care services for patients in hospitals using a number of variables in the hospital environment such as available resources, namely doctors, nursing medical personnel, technicians, technical equipment. This study is subject to the aim of minimizing all costs incurred to perform services, namely travel time from places to provide health services to patients, medical staff costs to provide services of the type of service, and so on. These variables are explained in the form of mathematical models that are able to explain existing constraints and minimize costs and time when performing services. The modeling results were tested using Linear Ineraktive Discrete Optimizer (LINDO) programming to determine errors that might occur in the model. The test results provide information that the maximum value of the objective function is 88.00 at the 25th iteration step so that the new model is expected to optimize health services for hospital patients and existing health clinics.

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Section: Methodsmentioning

confidence: 99%

Optimization of Health Care Services with Limited Resources

Pardede¹,

Mawengkang²,

Zarlis³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…This situation applies for other natural disasters. Some countries have experienced natural disasters more than others [8]. This situation has been the main reason why a country such as the US has spent a lot of efforts to deal with natural disasters.…”

Section: Tornado Recognitionmentioning

confidence: 99%

“…Some of them are still at the stage of now-casting [3] [4] [5] [6] [7], not yet forecasting. Some believe that the most difficult part of forecasting natural disasters is producing the mathematical formulas of it [8].…”

Section: Weather Data Handlings For Tornado Recognition Using Mhgn 1 Introductionmentioning

confidence: 99%

“…So, it is a strong indication that other methods than mathematical formulas to forecast a tornado are required. A number of researchers [8] have developed an artificial intelligent technology to forecast an upcoming tornado. Although there is no definite mathematical functions that can be used to determine the condition of a tornado, windspeed, wind-direction, air-temperature, and air-pressure that constitute a tornado, are all caused by physical states [9].…”

Section: Weather Data Handlings For Tornado Recognition Using Mhgn 1 Introductionmentioning

confidence: 99%

“…The physical patterns that have been used in mHGN [8] were not yet ready for forecasting tornados, although mHGN is able to recognize incomplete alphabetical patterns [9]. There are two major issues why mHGN is not ready to forecast tornados.…”

Section: Weather Data Handlings For Tornado Recognition Using Mhgn 1 Introductionmentioning

confidence: 99%

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Weather Data Handlings for Tornado Recognition Using mHGN

Nasution

Sembiring

Syahruddin

et al. 2019

IFIP Advances in Information and Communication Technology

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The usage of the mHGN as a pattern recognizer cannot necessarily be used to recognize tornados. Two important issues that need to be solved first are related to data handlings of not-accurately recorded data, and to those of complex weather data. The not-so-appropriate data handlings will produce high false positive and true negative rate of the recognition results. Yet, the latest development of those data handlings has been carried out, and has shown positive and promising results. Such a new approach of data handlings can, therefore, be used to improve the quality and the accuracy of forecasting a tornado. The results taken from a simulated circumstances of a multidimensional pattern recognition have shown, that the tornado can be recognized around 9 hours earlier with 90% of accuracy. However, several improvements related to the data representation within the mHGN architecture need to be implemented. The deployment of mHGN in several risky areas of tornados can then be expected as an alternative way of reducing damages, losses, and costs.

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Real-Time Tornado Forecasting Using SLHGN

Nasution

Sembiring

Syahruddin

et al. 2019

IFIP Advances in Information and Communication Technology

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The architecture of mHGN has been improved and become Single Layer Hierarchical Graph Neuron (SLHGN). The speed of this new architecture for recognizing multidimensional patterns is faster than the one of mHGN. It is therefore more suitable for forecasting multidimensional and complex process of tornado's genesis in real-time. Additionally, two important issues related to data handlings of non-accurate recorded data and data handlings of complex weather data have been solved. These improvements have given significant and positive quality of SLHGN in forecasting tornado. Although the accuracy and the forecasting performance cannot be calculated properly, due to the fact that weather data is not always available, the specific characteristics of the SLHGN experiment results show very promising values. This results suggest that tornado can be forecasted at least 5 hours before it occurs. People in the to-be-hit area will then have adequate time to be evacuated or to escape. The deployment of SLHGN in risky areas of tornados can then be expected as a tool for reducing damages, losses, and costs. Several improvements in weather station distribution still need to be carried out in order to improve the quality of tornado forecasting using SLHGN.

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Forecasting Natural Disasters of Tornados Using mHGN

Cited by 6 publications

References 6 publications

Optimization of Health Care Services with Limited Resources

Optimization of Health Care Services with Limited Resources

Weather Data Handlings for Tornado Recognition Using mHGN

Real-Time Tornado Forecasting Using SLHGN

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