Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent

In recent year, the rise of economic growth and technology advance leads to improve the quality of service of traditional transport system. Intelligent Transportation System (ITS) has become more and more popular. At present, the collection of real-time traffic information is executed in two ways: (1) Stationary Vehicle Detectors (VD) and (2) Global Position System (GPS)-based probe cars reporting. However, VD devices need a large sum of money to build and maintain. Therefore, we propose the linear regression model to infer the equation between vehicle speed and traffic flow. The traffic flow can be estimated from the speed which is obtained from GPS-based probe cars. In experiments, the Speed Error Ratio (SER) and Flow Error Ratio (FER) of linear regression model are 4.60% and 24.63% respectively. The estimated speed and traffic flow by using linear regression model is better than by using linear model, power law model, exponential model, and normal distribution model. Therefore, the linear regression model can be used to estimate traffic information for ITS.

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Real-time grower-finisher pigs’ growth monitored and forecasted using a dynamic linear regression model

10.13031/iles.18-133 ◽

2018 ◽

Author(s):

Alberto Peña Fernández ◽

Tomas Norton ◽

Erik Vranken ◽

Daniel Berckmans

Keyword(s):

Linear Regression ◽

Regression Model ◽

Real Time ◽

Linear Regression Model

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Plant Growth and LAI Estimation using quantized Embedded Regression models for high throughput phenotyping

10.21203/rs.3.rs-1060088/v1 ◽

2021 ◽

Author(s):

Dhruv Sheth

Keyword(s):

Image Processing ◽

Linear Regression ◽

Plant Growth ◽

Regression Model ◽

Real Time ◽

Crop Yield ◽

High Throughput ◽

Linear Regression Model ◽

Growth Patterns ◽

Biomass Estimation

Abstract Due to the influence of climate change, and due to it's unpredictable nature, majority of agricultural crops have been affected in terms of production and maintenance. Hybrid and cost-effective crops are making their way into the market, but monitoring factors which affect the increase in yield of these crops, and conditions favorable for growth have to be manually monitored and structured to yield high throughput. Farmers are showing transition from traditional means to hydroponic systems for growing annual and perennial crops. These crop arrays possess growth patterns which depend on environmental growth conditions in the hydroponic units. Semi-autonomous systems which monitor these growth may prove to be beneficial, reduce costs and maintenance efforts, and also predict future yield beforehand to get an idea on how the crop would perform. These systems are also effective in understanding crop drools and wilt/diseases from visual systems and traits of plants.Forecasting or predicting the crop yield well ahead of its harvest time would assist the strategists and farmers for taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. In this article, I~propose an Embedded Machine Learning approach to predicting crop yield and biomass estimation of crops using an Image based Regression approach using EdgeImpulse that runs on Edge system, Sony Spresense, in real time. This utilizes few of the 6 Cortex M4F cores provided in the Sony Spresense board for Image processing, inferencing and predicting a regression output in real time. This system uses Image processing to analyze the plant in a semi-autonomous environment and predict the numerical serial of the biomass allocated to the plant growth. This numerical serial contains a threshold of biomass which is then predicted for the plant. The biomass output is then also processed through a linear regression model to analyze efficacy and compared with the ground truth to identify pattern of growth. The image Regression and linear regression model contribute to an algorithm which is finally used to test and predict biomass for each plant semi-autonomously.

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Evaluation of MJO Forecast Skill from Several Statistical and Dynamical Forecast Models

Journal of Climate ◽

10.1175/2008jcli2421.1 ◽

2009 ◽

Vol 22 (9) ◽

pp. 2372-2388 ◽

Cited By ~ 106

Author(s):

Kyong-Hwan Seo ◽

Wanqiu Wang ◽

Jon Gottschalck ◽

Qin Zhang ◽

Jae-Kyung E. Schemm ◽

...

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Real Time ◽

Linear Regression Model ◽

Southern Oscillation ◽

Multiple Linear Regression Model ◽

Forecast Skill ◽

Maritime Continent ◽

Forecast System

Abstract This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP’s coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (>0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to ∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model. It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region.

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