scholarly journals Prediction model of dengue hemorrhagic fever transmission to enhance early warning system in Gergunung Village, Klaten District, Central Java

2019 ◽  
Vol 51 (03) ◽  
Author(s):  
Tri Baskoro Tunggul Satoto ◽  
Alfin Harjuno Dwiputro ◽  
Rifa Nadhifa Risdwiyanto ◽  
A. Ulil Fadli Hakim ◽  
Nur Alvira Pascawati ◽  
...  
Keyword(s):  
Prediction Model ◽  
Dengue Hemorrhagic Fever ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Hemorrhagic Fever ◽  
Central Java

scholarly journals Peran Juru Pantau Jentik dalam Sistem Kewaspadaan Dini Demam Berdarah Dengue di Indonesia

2012 ◽  
Vol 6 (6) ◽  
pp. 243 ◽  
Author(s):  
Diana Andriyani Pratamawati
Keyword(s):  
Literature Review ◽  
Dengue Hemorrhagic Fever ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Hemorrhagic Fever ◽  
Southeast Asian ◽  
Vector Borne ◽  
Dengue Prevention

Program pencegahan dan pemberantasan demam berdarah dengue (DBD) telah berlangsung sekitar 43 tahun dan berhasil menurunkan angka kema- tian dari 41,3% pada tahun 1968 menjadi 0,87% pada tahun 2010, tetapi belum berhasil menurunkan angka kesakitan. Bahkan, Indonesia men- duduki urutan tertinggi kasus DBD di Association of Southeast Asian Nations (ASEAN) pada tahun 2010. Salah satu faktor belum efektifnya pencegahan DBD di Indonesia adalah masih lemahnya sistem kewas- padaan dini. Peran juru pantau jentik (jumantik) sangat penting dalam sistem kewaspadaan dini mewabahnya DBD karena berfungsi untuk memantau keberadaan dan menghambat perkembangan awal dari vektor penular DBD. Seiring masih tingginya angka kasus DBD di Indonesia, muncul pertanyaan bagaimana peran jumantik dalam sistem kewaspadaan dini DBD selama ini di Indonesia. Artikel ini mencoba menelaah masalah tersebut berdasarkan tinjauan pustaka. Secara umum, peran jumantik dinilai cukup berhasil dalam pencegahan DBD, namun terdapat beberapa hal yang perlu menjadi bahan evaluasi.Kata kunci: Jumantik, demam berdarah dengue, sistem kewaspadaan diniAbstractPrograms of prevention and eradication of dengue hemorrhagic fever (DHF) has been around 43 years and managed to reduce mortality from 41,3% in 1968 to 0,87% in 2010, but has not managed to reduce morbidity. Indonesia even ranked the highest of dengue cases in Association of Southeast Asian Nations (ASEAN) by the year 2010. One factorthat made has not been effective dengue prevention in Indonesia is the early warning system is still weak. Jumantik role is very important in the early warning system outbreaks of dengue hemorrhagic fever because it serves to monitor the presence andinhibit the early development of vector-borne dengue fever. During the high number of dengue cases in Indonesia, question rouses how jumantik role in the dengue hemorrhagic fever early warning system so far in Indonesia. This article takes a closer look based on a literature review. In general, the role of jumantik considered quite successful in preventing dengue hemorrhagic fever early warning system but nevertheless there are things that need to be evaluated.Key words: Jumantik, dengue hemorrhagic fever, early warning system

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

scholarly journals Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Marović ◽  
Ivana Sušanj ◽  
Nevenka Ožanić
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Ann Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
The University ◽  
The Impact

The impact of natural disasters increases every year with more casualties and damage to property and the environment. Therefore, it is important to prevent consequences by implementation of the early warning system (EWS) in order to announce the possibility of the harmful phenomena occurrence. In this paper, focus is placed on the implementation of the EWS on the micro location in order to announce possible harmful phenomena occurrence caused by wind. In order to predict such phenomena (wind speed), an artificial neural network (ANN) prediction model is developed. The model is developed on the basis of the input data obtained by local meteorological station on the University of Rijeka campus area in the Republic of Croatia. The prediction model is validated and evaluated by visual and common calculation approaches, after which it was found that it is possible to perform very good wind speed prediction for time steps Δt=1 h, Δt=3 h, and Δt=8 h. The developed model is implemented in the EWS as a decision support for improvement of the existing “procedure plan in a case of the emergency caused by stormy wind or hurricane, snow and occurrence of the ice on the University of Rijeka campus.”

Prediction of and early Warning for Deformation and Stress in the Xiaowan Arch Dam during the First Impounding Stage

2013 ◽  
Vol 405-408 ◽  
pp. 2463-2472 ◽  
Author(s):  
Qiu Jing Zhou ◽  
Guo Xin Zhang ◽  
Yi Liu
Keyword(s):  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Arch Dam ◽  
Hybrid Prediction ◽  
Arch Dams ◽  
Model Based ◽  
Complete Process ◽  
High Arch Dams

A hybrid prediction model based on the simulation of the complete process in high arch dams is proposed because of capacity shortage in the prediction of and early warning for deformation and stress in such dams during the first impounding stage. An early warning system for dam body deformation as well as dam heel and dam toe stress is established in this model. The warning thresholds are also clarified. Applications in the first impounding stage of the Xiaowan Arch Dam show that the model is practical and effective, and can provide highly accurate predictions. Early warning indicator thresholds for deformation and stress are reasonably set to provide warnings for abnormal situations. The model provides accurate predictions and effective warning methods for the safe impounding and operation of high arch dams.

scholarly journals Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yoonhee Kim ◽  
J. V. Ratnam ◽  
Takeshi Doi ◽  
Yushi Morioka ◽  
Swadhin Behera ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Malaria Surveillance ◽  
Short Term ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Seasonal Climate Forecasts

AbstractAlthough there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

Exploration of the characteristics of landslide triggering rainfall using rain gauge and numerical weather prediction for Yogyakarta and Central Java, Indonesia

2020 ◽  
Author(s):  
Ratna Satyaningsih ◽  
Ardhasena Sopaheluwakan ◽  
Danang Eko Nuryanto ◽  
Tri Astuti Nuraini ◽  
Arif Rahmat Mulyana ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Early Warning ◽  
Early Warning System ◽  
Rainfall Intensity ◽  
Weather Prediction ◽  
Warning System ◽  
Rainfall Data ◽  
Numerical Weather ◽  
Central Java ◽  
Landslide Early Warning

<p>The existing Landslide Early Warning System (LEWS) for Indonesia was developed using rainfall thresholds, which were derived from the relationship between rainfall inducing landslides and landslide events in the past. The system utilized the median values of 1-day and 3-day cumulative observed rainfall for determining the threshold and a relatively limited number of landslide events throughout Indonesia during the period of the system development. The system employed a single set of threshold values for all regions despite the possibility of differences in rainfall intensity characteristics for each region. For prediction, the system used rainfall data derived from satellite products and rainfall forecast data with a spatial resolution of 0.25° x 0.25°, which is not adequate for catchment-scale landslide analysis.</p><p> </p><p>We attempt to improve the LEWS by applying a statistical approach based on rainfall intensity and duration for a longer time-series of data set. Instead of determining the thresholds for national scale, we focus on the Special Region of Yogyakarta and surrounding cities in Central Java which are prone to landslides but have high population density. In addition to that, we also perform preliminary exploration of the potential of the output of high-resolution numerical weather prediction in simulating the rainfall inducing the landslides for several historical landslide events. This study is part of a project called BILEWS, a Blueprint for an Indonesian Landslide Early Warning System, which aims to develop threshold for landslides and debris flows as the basis for early warning to be applied at several test sites in Java, using tailored rainfall data, combined with empirical and physically-based hydrological and landslide models, as well as historical landslide data.</p>

scholarly journals An integrated methodology to develop a standard for landslide early warning system

2016 ◽  
Author(s):  
Teuku Faisal Fathani ◽  
Dwikorita Karnawati ◽  
Wahyu Wilopo
Keyword(s):  
Early Warning ◽  
Disaster Preparedness ◽  
Early Warning System ◽  
Local Community ◽  
Warning System ◽  
Disaster Mitigation ◽  
Guidance System ◽  
Central Java ◽  
Response Team ◽  
Landslide Early Warning

Abstract. Landslides are one of the commonly occurring natural disasters with worldwide susceptibility. Some distinct features of these disasters are that the affected area has a high density of population, low accessibility and the locals have low level of knowledge about disaster mitigation. Considering these conditions, it is necessary to establish a standard for an early warning system specific to landslide disaster risk reduction. This standard is expected to be the guidance system in conducting detection, prediction, interpretation, and response in landslide disasters. This new standard introduces the seven sub-systems for landslide early warning, starting with risk assessment and mapping, dissemination and communication, establishment of disaster preparedness and response team, development of evacuation map, standardized operating procedures, installation of monitoring and warning services, and building a local commitment to the operation and maintenance of the entire program. Since 2012, Indonesia has implemented a trial for the seven sub-systems in 20 landslide-prone provinces throughout the country. An example of the application of the proposed methodology in a local community in Central Java, Indonesia is also described.

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