A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health

PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance.

Acquisition and Processing of Data in CPS for Remote Monitoring of the Human functional State

Data acquisition and processing in cyber-physical system for remote monitoring of the human functional state have been considered in the paper. The data processing steps, strategies for multi-step forecasting evaluation metrics and machine learning algorithms to be implemented have been analysed and described. What is important, this way it will be possible to track the condition of the sick and response to the health changes in advance.

DO WE NEED PRE-STORM SURVEYED BATHYMETRY FOR OPERATIONAL EROSION FORECASTING? EVALUATION OF REPRESENTATIVE AND SYNTHETIC BATHYMETRY ALTERNATIVES

Immediate pre-storm bathymetry is a key input required for numerical models used in coastal hazard Early Warning Systems. However, the expense and challenging nature of hydrographic surveying means that the availability of high-quality data is extremely rare. This study evaluates the extent to which synthetic and representative bathymetry alternatives can be used to obtain reliable predictions of storm induced sub-aerial erosion using the XBeach coastal erosion numerical model. Multiple storm events at 2 contrasting sites are modelled using 6 bathymetry scenarios including pre-storm surveyed bathymetries, an average bathymetry, and Dean profiles. The output is analysed to evaluate the skill of XBeach erosion predictions as a function of the bathymetry used.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/bE3aXVXxZqQ

Comparing GEFS, ECMWF, and Postprocessing Methods for Ensemble Precipitation Forecasts over Brazil

This study compares the performance of Global Ensemble Forecast System (GEFS) and European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation ensemble forecasts in Brazil and evaluates different analog-based methods and a logistic regression method for postprocessing the GEFS forecasts. The numerical weather prediction (NWP) forecasts were evaluated against the Physical Science Division South America Daily Gridded Precipitation dataset using both deterministic and probabilistic forecasting evaluation metrics. The results show that the ensemble precipitation forecasts performed commonly well in the east and poorly in the northwest of Brazil, independent of the models and the postprocessing methods. While the raw ECMWF forecasts performed better than the raw GEFS forecasts, analog-based GEFS forecasts were more skillful and reliable than both raw ECMWF and GEFS forecasts. The choice of a specific postprocessing strategy had less impact on the performance than the postprocessing itself. Nonetheless, forecasts produced with different analog-based postprocessing strategies were significantly different and were more skillful and as reliable and sharp as forecasts produced with the logistic regression method. The approach considering the logarithm of current and past reforecasts as the measure of closeness between analogs was identified as the best strategy. The results also indicate that the postprocessing using analog methods with long-term reforecast archive improved raw GEFS precipitation forecasting skill more than using logistic regression with short-term reforecast archive. In particular, the postprocessing dramatically improves the GEFS precipitation forecasts when the forecasting skill is low or below zero.

Forecasting Tourism Demand with Decomposed Search Cycles

This study aims to examine whether decomposed search engine data can be used to improve the forecasting accuracy of tourism demand. The methodology was applied to predict monthly tourist arrivals from nine countries to Hong Kong. Search engine data from Google Trends were first decomposed into different components using an ensemble empirical mode decomposition method and then the cyclical components were examined through statistical analysis. Forecasting models with rolling window estimation were implemented to predict the tourist arrivals to Hong Kong. Results indicate the proposed methodology can outperform the benchmark model in the out-of-sample forecasting evaluation of Choi and Varian (2012). The findings also demonstrate that our proposed methodology is superior in forecasting turning points. This study proposes a unique decomposition-based perspective on tourism forecasting using online search engine data.

PENERAPAN PROSES DAN TEKNIK PERAMALAN – STUDI KASUS DI MANUFAKTUR TRANSFORMER

Demand forecasting is essential for business processes and firm’s profitability. A Good demand forecasting requires the expertise and reliability of the planning staff. The research is a case study in a transformer manufacturer in Indonesia, which planned to apply forecasting process and suitable tools for their products. The study demonstrated a systematic step required in forecasting the four studied products. For each product, 6 forecast models were evaluated and the best model was selected by optimizing forecast parameters which give the least forecast errors. The chosen forecasting model is a simple model but fairly complete in forecasting evaluation, to accommodate both trend and seasonal possibility in product forecasting. The study offers the basis for the company to establish forecasting processes and tools for its products. It also recommended the studied company to perform and monitor its forecasting process for continuous improvement to its business process.