Demand Forecast of Geological Disaster Rescue Equipment Based on "Scenario-Task"

2021 ◽  
Author(s):  
Ruifang La ◽  
Liu Han ◽  
Pengfei Bai ◽  
Zaixu Zhang
Keyword(s):  
Demand Forecast ◽  
Geological Disaster ◽  
Scenario Task

Curriculum Development Training Improvement in the Competence Geological Disaster Mitigation Community Based

2017 ◽  
Vol 3 (2) ◽  
pp. 18
Author(s):  
Rusman ◽  
Asep Rohman
Keyword(s):  
Curriculum Development ◽  
Ground Motion ◽  
Educational Background ◽  
Disaster Mitigation ◽  
Needs Analysis ◽  
Community Based ◽  
Unique Region ◽  
Model Curriculum ◽  
Geological Conditions ◽  
Geological Disaster

This research was motivated still many natural disasters in Indonesia. Geological disasters would always be an important issue in the Indonesian Nation as a consequence Indonesia's geological conditions unique region, rich in natural resources but full of potential disasters. Disaster handling required the participation of all components of the nation led to the importance of the massive dissemination of disaster information to all levels of society. The role of the community in the society was considered to be very strategic as agents of change. Unfortunately, the competence of members of the community who were still considered weak in disaster mitigation and counseling techniques became constraints the achievement of objectives disaster-conscious society. Increased competence was absolutely necessary and training could be selected as an option to improve competence. Research conducted using the method of research and development which was divided into three main stages. First, the needs analysis as a preliminary study, the second, the development of a model curriculum, and the third trials of the curriculum model to determine the effectiveness in improving the competence fields of geological disaster mitigation. This study was conducted to determine the curriculum development process proper training to improve competence in community-based geological disaster mitigation. The results showed that the model developed training curriculum based on the needs analysis proven effective in improving participants's competence to do counseling disaster mitigation. Pre-post test results showed an increase in the cognitive aspects of participants in Trial I and Trial II. Significant improvement occurred on the competence of counsel which showed a success rate of Trial II in improving the competence of counsel practice of training participants. Factors supporting the development of a model curriculum Extension Disaster Mitigation Training Community-Based Ground Motion  were: (a) the competence of lecturers geological disaster mitigation; (B) the interests of members of community volunteers; and (c) the support of policy makers, while the factors that impeded the development of curriculum models were limited clump of competence training in geology, low educational background and knowledge of the geology and ground motion, and limited time.

scholarly journals Information Acquisition and Its Incentives in an E-Commerce Supply Chain under the Offline Showroom Model

2021 ◽  
Vol 16 (5) ◽  
pp. 1791-1804
Author(s):  
Mengli Li ◽  
Xumei Zhang
Keyword(s):  
Supply Chain ◽  
Information Acquisition ◽  
Incentive Contracts ◽  
Demand Forecast ◽  
Competition Intensity ◽  
Demand Information ◽  
Optimal Information ◽  
Acquisition Strategy ◽  
The One ◽  
The Impact

Recently, the showroom model has developed fast for allowing consumers to evaluate a product offline and then buy it online. This paper aims at exploring the optimal information acquisition strategy and its incentive contracts in an e-commerce supply chain with two competing e-tailers and an offline showroom. Based on signaling game theory, we build a mathematical model by considering the impact of experience service and competition intensity on consumers’ demand. We find that, on the one hand, information acquisition promotes supply chain members to obtain demand information directly or indirectly, which leads to forecast revenue. On the other hand, information acquisition promotes supply chain members to distort optimal decisions, which results in signal cost. The optimal information acquisition strategy depends on the joint impact of forecast revenue, signal cost and demand forecast cost. Notably, in some conditions, the offline showroom will not acquire demand information even when its cost is equal to zero. We also design two different information acquisition incentive contracts to obtain Pareto improvement for all supply chain members.

scholarly journals Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2151
Author(s):  
Feras Alasali ◽  
Husam Foudeh ◽  
Esraa Mousa Ali ◽  
Khaled Nusair ◽  
William Holderbaum
Keyword(s):  
Renewable Energy ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Golden Ratio ◽  
Clean Energy ◽  
Energy Sources ◽  
Percentage Error ◽  
Ann Model ◽  
Demand Forecast ◽  
Pv System

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

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