Machine Learning Methods to Infer Precipitation Phase from Temperature and Moisture Profiles

2021 ◽  
Author(s):  
Dominique Brunet ◽  
John Rafael Ranieses Quinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Relative Humidity ◽  
Dew Point ◽  
Total Precipitation ◽  
Direct Observations ◽  
Snow Storage ◽  
Weather Stations ◽  
Moisture Profiles ◽  
Precipitation Phase

<p>The phase of falling precipitation can have a large societal impact for both hydrology (snow storage, rain-on-snow events), meteorology (snowstorms, freezing rain) and climate (snow albedo feedback). In Canada, many surface weather stations report precipitation information in the form of total precipitation (liquid-equivalent), but very few weather stations directly report snow. Thus, precipitation phase must be inferred from ancillary data such as temperature and moisture. Each scientific community has developed its own tool for the determination phase in the absence of direct observations: from simple rules based on air temperature, dew point temperature or wet bulb temperature to sophisticated microphysics schemes passing by methods based on the discrimination of features extracted from vertical temperature profiles. With the recent advances of machine learning, there is an opportunity to investigate another set of methods based on deep neural networks.</p><p>Using ERA5 and ERA5-Land model re-analyses as the reference, we trained several recurrent neural networks (RNN) on vertical profiles of temperature and moisture to infer the snow fraction – the ratio of solid precipitation to total precipitation. Since precipitation phase (solid, liquid or mixed) was not directly available in the model re-analysis, we defined it using two thresholds: snow fraction of less than 5% for liquid, snow fraction of more than 95% for solid phase, and mixed phase for everything in between. The best performing neural network for regressing snow fraction is found to be a Gated Recurrent Unit (GRU) RNN using profiles up to 500 hPa above the surface of both temperature and relative humidity. A slight decrease in performance is observed if profiles up to 700 hPa are used instead. A feature experiment also reveals that the performance is significantly better when using both temperature and moisture profiles, but it does not really matter what type of moisture observations are used (either dew point spread, wet bulb temperature or relative humidity). For classifying precipitation phase, the balanced accuracy is over 90%, clearly outperforming the implementation of Bourgouin’s method used operationally in part of Canada. Compared with the K-Nearest Neighborhood (KNN) method trained on surface observations only, it is seen that the greatest gain in performance for GRU-RNN is when the surface temperature is close to zero degrees Celsius.</p><p>These preliminary results indicate the great potential of the proposed algorithm for determining snow fraction and precipitation phase in the absence of direct observations. The proposed algorithm could potentially be used for inferring snow fraction and precipitation phase in several applications such as (1) precipitation analysis for forcing hydrological models, (2) weather nowcasting, (3) weather forecast post-processing and (4) climate change impact studies.</p><p> </p>

scholarly journals The artificial intelligence system in prediction of the prevalence of acute coronary syndromes based on weather conditions

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P Molek ◽  
A Wlodarczyk ◽  
B Bochenek ◽  
A Wypych ◽  
J Nessler ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Relative Humidity ◽  
Atmospheric Pressure ◽  
Weather Conditions ◽  
Dew Point ◽  
Artificial Intelligence System ◽  
Dew Point Temperature ◽  
Intelligence System ◽  
Coronary Syndromes

Abstract Background It was shown that different individual weather conditions are associated with the incidence of acute coronary syndromes (ACS). Despite this, the prediction of the number of ACS depending on the weather conditions in the given place and time is not effective to date. Purpose We sought to investigate whether the artificial intelligence system might be useful in prediction of the prevalence of ACS based on weather conditions. Methods In this study, data of 159307 consecutive patients obtained from National Health Service registry, hospitalized due to ACS in Lesser Poland Province (province area of 15008 km2, population of 3.4 M in 2014) between 2008 and 2018 have been compared with meteorological conditions collected in the Institute of Meteorology and Water Management from five weather stations scattered across Lesser Poland Province. Because of small sample size in three of them, only data from two stations (Krakow-Balice, n=75565, Tarnow, n=30079) were used for further analysis. In four separate seasons, the number of ACS events in each day was compared with meteorological conditions on a given day and six days before. We analysed weather conditions such as: wind 10 metres above ground (W_10), temperature (T), dew point temperature (T_dp), relative humidity 2 metres above the ground (Hum_2), atmospheric pressure reduced to mean sea level (Pres), atmospheric precipitation (Prec), and 3 hours atmospheric pressure changes (Pres_3h). For all parameters extreme (maximum – max, minimum – min) values and ranges of these parameters in each day were analysed. All data were used in a system based on machine learning (Random Forest), which allowed to create a model that predicted the incidence of ACS and to determine importance of each inputted weather parameter in this prediction. Results All weather parameters were divided into machine learning data (70%) and test data (30%) to verify functioning of the model. The correlation between real number of ACS and predicted number of ACS for two meteorological stations for spring ranges from 0.69 to 0.71 with confidence intervals (CI) of 0.63–0.77, for summer the correlation was 0.66–0.75 with CI of 0.59–79, for autumn 0.69–0.74 with CI of 0.63–0.79 and for winter 0.69–0.72 with CI 0.63–0.77 (P<0.0001 for each prediction, example of prediction in the Figure 1A). Among all analysed meteorological parameters the most important in the machine learning were range of relative humidity, range of dew point temperature and maximal relative humidity (Figure 1B). Conclusions Artificial intelligence system seems to be useful in predicting the prevalence of ACS with model based on weather conditions. Figure 1. ACS prediction for summer in Krakow Funding Acknowledgement Type of funding source: None

scholarly journals Seasonal Dynamics of Photochemical Performance of PS II of Terrestrial Mosses from Different Elevations

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2613
Author(s):  
Jiewei Hao ◽  
Xueyan Xu ◽  
Lina Zhang
Keyword(s):  
Relative Humidity ◽  
Seasonal Dynamics ◽  
Climatic Factors ◽  
Assimilation Efficiency ◽  
Dew Point ◽  
Ps Ii ◽  
Moss Species ◽  
Tropical Regions ◽  
Weather Stations ◽  
Terrestrial Mosses

Mosses are critical components of tropical forest ecosystems and have multiple essential ecological functions. The drying and rehydrating and often hot environments in tropical regions present some of the greatest challenges for their photosynthetic activities. There is limited knowledge available on the physiological responses to the changing environments such as temperature and water pattern changes for terrestrial mosses. We examined the seasonal dynamics of photochemical performance of PS II through the measuring of chlorophyll fluorescence of 12 terrestrial mosses in situ from five different elevations by Photosynthesis Yield Analyzer MINI-PAM-II, along with the seasonal changes of climatic factors (air temperature, dew point, relative humidity and rainfall), which were collected by local weather stations and self-deployed mini weather stations. The results showed a great seasonality during observing periods, which, mainly the changes of rainfall and relative humidity pattern, presented significant impacts on the photochemical performance of PS II of terrestrial mosses. All these tested moss species developed a suitable regulated and non-regulated strategy to avoid the detrimental effect of abiotic stresses. We found that only Hypnum plumaeforme, Pterobryopsis crassicaulis and Pogonatum inflexum were well adapted to the changes of habitat temperature and water patterns, even though they still experienced a lower CO2 assimilation efficiency in the drier months. The other nine species were susceptible to seasonality, especially during the months of lower rainfall and relative humidity when moss species were under physiologically reduced PS II efficiency. Anomobryum julaceum, Pogonatum neesii, Sematophyllum subhumile, Pseudotaxiphyllum pohliaecarpum and Leucobryum boninense, and especially Brachythecium buchananii, were sensitive to the changes of water patterns, which enable them as ideal ecological indicators of photosynthetic acclimation to stressed environments as a result of climate change.

scholarly journals Rice Blast (Magnaporthe oryzae) Occurrence Prediction and the Key Factor Sensitivity Analysis by Machine Learning

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 771
Author(s):  
Li-Wei Liu ◽  
Sheng-Hsin Hsieh ◽  
Su-Ju Lin ◽  
Yu-Min Wang ◽  
Wen-Shin Lin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sensitivity Analysis ◽  
Relative Humidity ◽  
Solar Energy ◽  
Soil Temperature ◽  
Air Temperature ◽  
Rice Blast ◽  
Dew Point ◽  
Average Relative Humidity

This study aimed to establish a machine learning (ML)-based rice blast predicting model to decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature and solar energy were selected for model development. The developed multilayer perceptron (MLP), support vector machine (SVM), Elman recurrent neural network (Elman RNN) and probabilistic neural network (PNN) were evaluated by F-measures. Finally, a sensitivity analysis (SA) was conducted for the factor importance assessment. The study result shows that the PNN performed best with the F-measure (β = 2) of 96.8%. The SA was conducted in the PNN model resulting in the main effect period is 10 days before the rice blast happened. The key factors found are minimum air temperature, followed by solar energy and equaled sensitivity of average relative humidity, maximum air temperature and soil temperature. The temperature phase lag in air and soil may cause a lower dew point and suitable for rice blast pathogens growth. Through this study’s results, rice blast warnings can be issued 10 days in advance, increasing the response time for farmers preparing related preventive measures, further reducing the losses caused by rice blast.

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals Trigonometric Inference Providing Learning in Deep Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6704
Author(s):  
Jingyong Cai ◽  
Masashi Takemoto ◽  
Yuming Qiu ◽  
Hironori Nakajo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Trigonometric Approximation ◽  
Model Parameters ◽  
Training Algorithms ◽  
Activation Functions ◽  
Classical Training ◽  
Sum Formula

Despite being heavily used in the training of deep neural networks (DNNs), multipliers are resource-intensive and insufficient in many different scenarios. Previous discoveries have revealed the superiority when activation functions, such as the sigmoid, are calculated by shift-and-add operations, although they fail to remove multiplications in training altogether. In this paper, we propose an innovative approach that can convert all multiplications in the forward and backward inferences of DNNs into shift-and-add operations. Because the model parameters and backpropagated errors of a large DNN model are typically clustered around zero, these values can be approximated by their sine values. Multiplications between the weights and error signals are transferred to multiplications of their sine values, which are replaceable with simpler operations with the help of the product to sum formula. In addition, a rectified sine activation function is utilized for further converting layer inputs into sine values. In this way, the original multiplication-intensive operations can be computed through simple add-and-shift operations. This trigonometric approximation method provides an efficient training and inference alternative for devices with insufficient hardware multipliers. Experimental results demonstrate that this method is able to obtain a performance close to that of classical training algorithms. The approach we propose sheds new light on future hardware customization research for machine learning.

scholarly journals Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4846
Author(s):  
Dušan Marković ◽  
Dejan Vujičić ◽  
Snežana Tanasković ◽  
Borislav Đorđević ◽  
Siniša Ranđić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Weather Conditions ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Lower Percentage ◽  
Timely Manner ◽  
Proposed Model ◽  
Set Up ◽  
Accuracy Of Prediction

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

Sign in / Sign up

Export Citation Format

Share Document