Modeling of tropical cyclone activity over the North Indian Ocean using generalised additive model and machine learning techniques: role of Boreal summer intraseasonal oscillation

2021 ◽  
Author(s):  
Md Wahiduzzaman ◽  
Jing-Jia Luo
Keyword(s):  
Additive Model ◽  
Intraseasonal Oscillation ◽  
North Indian Ocean ◽  
Machine Learning Techniques ◽  
Boreal Summer ◽  
Cyclone Activity ◽  
Generalised Additive Model ◽  
The North ◽  
Learning Techniques

scholarly journals The role of conidia in the dispersal of Ascochyta rabiei

2020 ◽  
Author(s):  
Ihsanul Khaliq ◽  
Joshua Fanning ◽  
Paul Melloy ◽  
Jean Galloway ◽  
Kevin Moore ◽  
...  
Keyword(s):  
Additive Model ◽  
Sprinkler Irrigation ◽  
Ascochyta Rabiei ◽  
Strong Wind ◽  
Generalised Additive Model ◽  
Rainfall Events ◽  
Plant Parts ◽  
Trap Plants ◽  
Further Development

AbstractAscochyta rabiei asexual spores (conidia) were assumed to spread over short distances (∼10 m) in a combination of rain and strong wind. The potential distance of conidial spread was investigated in three rainfall and three sprinkler irrigation events. Chickpea trap plants were distributed at the distances of 0, 10, 25, 50 and 75 m from infected chickpea plots before scheduled irrigation and forecast rainfall events. Trap plants were transferred to a controlled temperature room (20 °C) for 48 h (100% humidity) after being exposed in the field for 2–6 days for rainfall events, and for one day for irrigation events. After a 48 h incubation period, trap plants were transferred to a glasshouse (20 °C) to allow lesion development. Lesions on all plant parts were counted after two weeks, which gave an estimate of the number of conidia released and the distance travelled. Trap plants at all distances were infected in all sprinkler irrigation and rainfall events. The highest number of lesions on trap plants were recorded closest to the infected plots – the numbers decreased as the distance from the infected plots increased. There was a positive relationship between the amount of rainfall and the number of lesions recorded. A generalised additive model was developed that efficiently described spatial patterns of conidial spread. With further development, the model can be used to predict the spread of A. rabiei. This is the first systematic study to show that conidia distribute A. rabiei over longer distances than previously reported.

Machine Learning Techniques for Underwater Wireless Sensor Networks

2021 ◽  
pp. 194-211
Author(s):  
Deepti Rani ◽  
Anju Sangwan ◽  
Anupma Sangwan ◽  
Tajinder Singh
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Seeking ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques ◽  
Primary Focus

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

Potential role of machine learning techniques for modeling the hardness of OPH steels

2020 ◽  
pp. 101806
Author(s):  
Omid Khalaj ◽  
Moslem Ghobadi ◽  
Alireza Zarezadeh ◽  
Ehsan Saebnoori ◽  
Hana Jirková ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Role ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Using AI Methods to Evaluate a Minimal Model for Perception

2019 ◽  
Vol 2 (1) ◽  
pp. 503-524 ◽  
Author(s):  
Robert Prentner ◽  
Chris Fields
Keyword(s):  
Conceptual Understanding ◽  
Single Agent ◽  
Psychological Problem ◽  
Machine Learning Techniques ◽  
Distributed Network ◽  
Perceptual Inference ◽  
Interacting Agents ◽  
Learning Techniques ◽  
The Relationship

AbstractThe relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, but interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research.While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation.

The North Pacific Pacemaker Effect on Historical ENSO and Its Mechanisms

2019 ◽  
Vol 32 (22) ◽  
pp. 7643-7661 ◽  
Author(s):  
Dillon J. Amaya ◽  
Yu Kosaka ◽  
Wenyu Zhou ◽  
Yu Zhang ◽  
Shang-Ping Xie ◽  
...  
Keyword(s):  
El Niño ◽  
North Pacific ◽  
El Nino ◽  
Southern Oscillation ◽  
Deep Convection ◽  
Boreal Summer ◽  
Enso Events ◽  
The North ◽  
The North Pacific

Abstract Studies have indicated that North Pacific sea surface temperature (SST) variability can significantly modulate El Niño–Southern Oscillation (ENSO), but there has been little effort to put extratropical–tropical interactions into the context of historical events. To quantify the role of the North Pacific in pacing the timing and magnitude of observed ENSO, we use a fully coupled climate model to produce an ensemble of North Pacific Ocean–Global Atmosphere (nPOGA) SST pacemaker simulations. In nPOGA, SST anomalies are restored back to observations in the North Pacific (>15°N) but are free to evolve throughout the rest of the globe. We find that the North Pacific SST has significantly influenced observed ENSO variability, accounting for approximately 15% of the total variance in boreal fall and winter. The connection between the North and tropical Pacific arises from two physical pathways: 1) a wind–evaporation–SST (WES) propagating mechanism, and 2) a Gill-like atmospheric response associated with anomalous deep convection in boreal summer and fall, which we refer to as the summer deep convection (SDC) response. The SDC response accounts for 25% of the observed zonal wind variability around the equatorial date line. On an event-by-event basis, nPOGA most closely reproduces the 2014/15 and the 2015/16 El Niños. In particular, we show that the 2015 Pacific meridional mode event increased wind forcing along the equator by 20%, potentially contributing to the extreme nature of the 2015/16 El Niño. Our results illustrate the significant role of extratropical noise in pacing the initiation and magnitude of ENSO events and may improve the predictability of ENSO on seasonal time scales.

Evolving a design focus in response to design solutions

1996 ◽  
Vol 10 (2) ◽  
pp. 161-162
Author(s):  
Mary Lou Maher
Keyword(s):  
Good Description ◽  
Problem Formulation ◽  
Machine Learning Techniques ◽  
Solution Synthesis ◽  
Traditional Treatment ◽  
Design Alternatives ◽  
Learning Techniques ◽  
Computer Based ◽  
Design Solutions

Most computer-based design tools assume designers work with a well-defined problem. The traditional treatment of design as two discrete phases; problem formulation and solution synthesis, is challenged by recent research. Though the view on discrete phases may be applicable to simple and/or well-defined design tasks, current research (Jonas, 1993; Logan & Smithers, 1993; Gero, 1994; Smithers et al., 1994) has shown that design is an ill-structured problem and the discrete phases view is not a good description of the process during which design alternatives are generated. A potential role of machine learning techniques is to provide a computational model of the changing representation of the design problem in response to the search for design solutions.

scholarly journals Secondary convective rings in an intense asymmetric cyclone of the Bay or Bengal

MAUSAM ◽  
2021 ◽  
Vol 48 (2) ◽  
pp. 273-282
Author(s):  
AKHILESH GUPTA ◽  
U. C. MOHANTY
Keyword(s):  
Indian Ocean ◽  
East Coast ◽  
Structural Characteristics ◽  
North Indian Ocean ◽  
Wall Structure ◽  
Sea Level Pressure ◽  
Cyclonic Storm ◽  
The North ◽  
First Time

ABSTRACT. The severe cyclonic storm with a core of hurricane winds of 4-11 May 1990, which crossed the Indian east coast near Machilipatnarn (Andhra Pradesh), was one of the most intense cyclones in recent years over the Bay of Bengal region of the north Indian Ocean. The storm reported the minimum sea level pressure of 912 hPa, the lowest observed value for any cyclone in the region. The storm exhibited certain interesting structural characteristics. The most striking  feature observed was the formation of secondary convective rings wrapped around the primary eyewall. These features were observed for nearly two days by four cyclone detection radars (CDR) located on the east coast of India. The paper presents an analysis of these features. We find that the double eye-wall structure of the storm has undergone a repetitive cycle characterized by the contraction of the outer eyewall and the weakening of the inner eyewall during the life of the cyclone. These interesting characteristics are observed for the first time in the north Indian Ocean for any cyclone. Some of the related aspects of double eyewall features, such as, the possible role of double eyewall structure on the recurvature or turning of the storm and the effect of land obstacle in the development of a secondary eyewall are discussed.        

Sign in / Sign up

Export Citation Format

Share Document