scholarly journals Analysis of the Relationship between Banded Orographic Convection and Atmospheric Properties Using Factorial Discriminant Analysis and Neural Networks

2010 ◽  
Vol 49 (4) ◽  
pp. 646-663 ◽  
Author(s):  
A. Godart ◽  
E. Leblois ◽  
S. Anquetin ◽  
N. Freychet
Keyword(s):  
Neural Networks ◽  
Potential Temperature ◽  
Rain Gauge ◽  
Meteorological Model ◽  
Rainfall Events ◽  
Moisture Fluxes ◽  
Orographic Convection ◽  
Discriminant Factorial Analysis ◽  
New Variables ◽  
The Relationship

Abstract The relationship between banded orographic convection and atmospheric properties is investigated for a region in the south of France where the associated rainfall events are thought to represent a significant portion of the hydrologic input. The purpose is to develop a method capable of producing an extensive database of banded orographic convection rainfall events from atmospheric sounding data for this region where insufficient rain gauge data and little or no suitable radar or satellite data are available. Two statistical methods—discriminant factorial analysis (DFA) and neural networks (NNs)—are used to determine 16 so-called elaborated nonlinear variables that best identify rainfall events related to banded orographic convection from atmospheric soundings. The approach takes rainfall information into account indirectly because it “learns” from the results of a previous study that explored meteorological and available rainfall databases, even if incomplete. The new variables include wind shear, low-level moisture fluxes, and gradients of the potential temperature in the lower layers of the atmosphere, and they were used to create an extensive database of banded orographic convection events from the archive of atmospheric soundings. Results of numerical simulations using the nonhydrostatic mesoscale (Méso-NH) meteorological model validate this approach and offer interesting perspectives for the understanding of the physical processes associated with banded orographic convection. DFA proves to be useful to determine the most discriminant factors with a physical meaning. Neural networks provide better results, but they do not allow for physical interpretation. The best solution is therefore to use the two methods together.

Assessment of »True« Variability of Small Rainfall Events in a Small Mountainous Watershed in the Summer

1982 ◽  
Vol 13 (4) ◽  
pp. 205-212 ◽  
Author(s):  
Lawrence C. Nkemdirim ◽  
Brian D. Meller
Keyword(s):  
Spatial Distribution ◽  
Standard Error ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Rainfall Events ◽  
Mountainous Watershed ◽  
Rainfall Volume ◽  
Areal Rainfall ◽  
The Relationship ◽  
Rain Gauge Network

The standard error of mean areal rainfall was calculated for various densities of rain gauge network in a small mountainous watershed in the summer of 1978. It is shown that a) the optimum gauge density required to assess mean rainfall is about 3 gauges/km2; b) the »true« variability in the spatial distribution of rainfall decreases with increasing rainfall amount; and c) the relationship between »true« variability and rainfall volume is linear in that watershed.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

scholarly journals Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan

2021 ◽  
Vol 10 (4) ◽  
pp. 209
Author(s):  
Chih-Ming Tseng ◽  
Yie-Ruey Chen ◽  
Chwen-Ming Chang ◽  
Yung-Sheng Chue ◽  
Shun-Chieh Hsieh
Keyword(s):  
Satellite Image ◽  
Extreme Rainfall ◽  
Land Area ◽  
Rainfall Events ◽  
Land Disturbance ◽  
Bare Land ◽  
Southern Taiwan ◽  
The Impact ◽  
Landslide Distribution ◽  
The Relationship

This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. The genetic adaptive neural network was used in combination with the texture analysis of the geographic information system for satellite image classification and interpretation to analyze land-use change and retrieve disaster records and surface information after five rainfall events from Typhoon Morakot (2009) to Typhoon Nanmadol (2011). The results revealed that except for extreme Morakot rains, the greater the degree of slope disturbance after rain, the larger the exposed slope. Extreme rainfall similar to Morakot strikes may have a greater impact on the bare land area than on slope disturbance. Moreover, the relationship between the bare land area and the index of land disturbance condition (ILDC) is positive, and the ratio of the bare land area to the quantity of bare land after each rainfall increases with the ILDC. With higher effective accumulative rainfall on the slope in the study area or greater slope disturbance, the landslide area at the second landslide point tended to increase.

Dream Bizarreness as the Cognitive Correlate of Altered Neuronal Behavior in REM Sleep

1989 ◽  
Vol 1 (3) ◽  
pp. 201-222 ◽  
Author(s):  
Adam N. Mamelak ◽  
J. Allan Hobson
Keyword(s):  
Neural Networks ◽  
Cerebral Cortex ◽  
Rem Sleep ◽  
Neuronal Dynamics ◽  
Neurophysiological Model ◽  
The Relationship ◽  
Cognitive Feature ◽  
The Brain ◽  
Cognitive Correlate ◽  
Raphe Neurons

Bizarreness is a cognitive feature common to REM sleep dreams, which can be easily measured. Because bizarreness is highly specific to dreaming, we propose that it is most likely brought about by changes in neuronal activity that are specific to REM sleep. At the level of the dream plot, bizarreness can be defined as either discontinuity or incongruity. In addition, the dreamer's thoughts about the plot may be logically deficient. We propose that dream bizarreness is the cognitive concomitant of two kinds of changes in neuronal dynamics during REM sleep. One is the disinhibition of forebrain networks caused by the withdrawal of the modulatory influences of norepinephrine (NE) and serotonin (5HT) in REM sleep, secondary to cessation of firing of locus coeruleus and dorsal raphe neurons. This aminergic demodulation can be mathematically modeled as a shift toward increased error at the outputs from neural networks, and these errors might be represented cognitively as incongruities and/or discontinuities. We also consider the possibility that discontinuities are the cognitive concomitant of sudden bifurcations or “jumps” in the responses of forebrain neuronal networks. These bifurcations are caused by phasic discharge of pontogeniculooccipital (PGO) neurons during REM sleep, providing a source of cholinergic modulation to the forebrain which could evoke unpredictable network responses. When phasic PGO activity stops, the resultant activity in the brain may be wholly unrelated to patterns of activity dominant before such phasic stimulation began. Mathematically such sudden shifts from one pattern of activity to a second, unrelated one is called a bifurcation. We propose that the neuronal bifurcations brought about by PGO activity might be represented cognitively as bizarre discontinuities of dream plot. We regard these proposals as preliminary attempts to model the relationship between dream cognition and REM sleep neurophysiology. This neurophysiological model of dream bizarreness may also prove useful in understanding the contributions of REM sleep to the developmental and experiential plasticity of the cerebral cortex.

scholarly journals Seasonal Variation of the Diurnal Cycle of Rainfall in Southern Contiguous China

2008 ◽  
Vol 21 (22) ◽  
pp. 6036-6043 ◽  
Author(s):  
Jian Li ◽  
Rucong Yu ◽  
Tianjun Zhou
Keyword(s):  
Seasonal Variation ◽  
Diurnal Cycle ◽  
Radiative Forcing ◽  
Early Morning ◽  
Rain Gauge ◽  
Rainfall Events ◽  
Late Afternoon ◽  
Long Duration ◽  
China Region ◽  
Afternoon Peak

Abstract Hourly station rain gauge data are employed to study the seasonal variation of the diurnal cycle of rainfall in southern contiguous China. The results show a robust seasonal variation of the rainfall diurnal cycle, which is dependent both on region and duration. Difference in the diurnal cycle of rainfall is found in the following two neighboring regions: southwestern China (region A) and southeastern contiguous China (region B). The diurnal cycle of annual mean precipitation in region A tends to reach the maximum in either midnight or early morning, while precipitation in region B has a late-afternoon peak. In contrast with the weak seasonal variation of the diurnal phases of precipitation in region A, the rainfall peak in region B shifts sharply from late afternoon in warm seasons to early morning in cold seasons. Rainfall events in south China are classified into short- (1–3 h) and long-duration (more than 6 h) events. Short-duration precipitation in both regions reaches the maximum in late afternoon in warm seasons and peaks in either midnight or early morning in cold seasons, but the late-afternoon peak in region B exists during February–October, while that in region A only exists during May–September. More distinct differences between regions A and B are found in the long-duration rainfall events. The long-duration events in region A show dominant midnight or early morning peaks in all seasons. But in region B, the late-afternoon peak exists during July–September. Possible reasons for the difference in the diurnal cycle of rainfall between the two regions are discussed. The different cloud radiative forcing over regions A and B might contribute to this difference.

Modeling the relationship between corporate strategy and wealth creation using neural networks

2000 ◽  
Vol 27 (11-12) ◽  
pp. 1077-1092 ◽  
Author(s):  
Caron H.St. John ◽  
Nagraj Balakrishnan ◽  
James O. Fiet
Keyword(s):  
Neural Networks ◽  
Corporate Strategy ◽  
Wealth Creation ◽  
The Relationship

Evaluation and Modeling of Passenger Vehicle Celeration Performance Based on Artificial Neural Networks

2015 ◽  
Vol 744-746 ◽  
pp. 1938-1942
Author(s):  
Yi He ◽  
Duan Feng Chu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comfort Level ◽  
Subjective Rating ◽  
Acceleration Data ◽  
Acceleration And Deceleration ◽  
The Subject ◽  
Artificial Neural ◽  
The Relationship ◽  
Subjective Sensation

As the siginificant factors influence passengers comfort, the vehicle celebration performance may easy to cause accidents, such as hard acceleration and deceleration performance. In order to find the relationship between passengers comfort and celebration performance, 35 passengers and three professional drivers were recruited in the field experiment. The passengers’ comfort feelings were analysed by subject questionnaires, the acceleration and deceleration data were received by CAN bus.The Artificial Neural Networks (ANNs) model was elaborated to estimate and predict the passengers comfort level of driver unsafe acceleration behavior situations. Therefore, the subject views of the passengers could be compared to object acceleration data. An ANN is applied to interconnect output data (subjective rating) with input data (objective parameters). Finally, it is found the investigatioin have demonstrated that the objective values are efficiently correlated with the subjective sensation. Thus, the presented approach can be effectively applied to support the drive train development of bus.

Research on the Realization of the Maximum Benefit of Thermal Power

2015 ◽  
Vol 713-715 ◽  
pp. 660-663
Author(s):  
Jia Min Chen
Keyword(s):  
Real Time ◽  
Oxygen Content ◽  
Thermal Power ◽  
Balance Method ◽  
Excess Air ◽  
Maximum Benefit ◽  
Function Expression ◽  
New Variables ◽  
The Relationship

First, Anti-balance method is used to build the model of q2,q3,q4 to figure out the Function expression of q2+q3+q4 .when q2+q3+q4 gets the minimum, the corresponded to the excess air ratio is the best excess air ratio. The excess air ratio is related to the load of boiler, so the function image describing the relationship between q2+q3+q4 and excess air ratio under the different load of 192.3MW, 215.8MW, 245.3MW and 298MW are made to get the best excess air ratio. Second, based on the model before, new variables q5 and q6 are added to complete the function formula of the efficiency and the excess air ratio, and four function image will be drew to show the tends. Finally, based on the conclusions above, smoke vents oxygen content can take the place of excess air ratio to achieve the purpose of monitoring the boiler in real time.

Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks

2010 ◽  
Vol 102-104 ◽  
pp. 846-850
Author(s):  
Wen Yu Pu ◽  
Yan Nian Rui ◽  
Lian Sheng Zhao ◽  
Chun Yan Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Selection System ◽  
Process Data ◽  
Replacement Strategy ◽  
Machining Quality ◽  
Main Factors ◽  
Artificial Neural ◽  
Neural Networks Optimization ◽  
The Relationship

Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence, realize the purpose of intelligence selection honing parameter through copying, exchanging, aberrance, replacement strategy and neural networks training. Besides, experiment was performed and the results helped optimize the theories model. Both the theory and experiment show the updated level and feasibility of this system.

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