Predictions of Disease Spikes Induced by Climate Variability: A Pilot Real Time Forecasting Model Project from Maharashtra, India

2021 ◽  
pp. 229-239
Author(s):  
Sujata Saunik ◽  
Pratip Shil ◽  
Subrata N. Das ◽  
Sangita P. Rajankar ◽  
Omkar Khare ◽  
...  
Keyword(s):  
Climate Variability ◽  
Real Time ◽  
Forecasting Model ◽  
Model Project

Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model

2019 ◽  
Vol 33 (14) ◽  
pp. 4799-4820 ◽  
Author(s):  
P. Shirisha ◽  
K. Venkata Reddy ◽  
Deva Pratap
Keyword(s):  
Real Time ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Time Flow

scholarly journals A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

2017 ◽  
Vol 88 ◽  
pp. 151-167 ◽  
Author(s):  
Xingya Xu ◽  
Xuesong Zhang ◽  
Hongwei Fang ◽  
Ruixun Lai ◽  
Yuefeng Zhang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Model Based ◽  
Filter Approach

scholarly journals The Applicability of Real Time Flood Forecasting Model: A Case Study in Thailand

1996 ◽  
Vol 40 ◽  
pp. 139-144
Author(s):  
Uruya Weesakul ◽  
Chaisak Sripadungtham
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Forecasting Model

FlashP

2021 ◽  
Vol 14 (5) ◽  
pp. 721-729
Author(s):  
Shuyuan Yan ◽  
Bolin Ding ◽  
Wei Guo ◽  
Jingren Zhou ◽  
Zhewei Wei ◽  
...  
Keyword(s):  
Real Time ◽  
Time Series Data ◽  
Real Data ◽  
Series Data ◽  
High Dimensional ◽  
Forecasting Model ◽  
Business Decisions ◽  
Forecasting Models ◽  
Sampling Algorithms ◽  
Forecasting System

Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution its alternatives on real data.

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