scholarly journals The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting

2022 ◽  
Vol 8 (2) ◽  
pp. 127-136
Author(s):  
Rahmatia Susanti ◽  
S. Supriatna ◽  
R. Rokhmatulah ◽  
Masita Dwi Mandini Manessa ◽  
Aris Poniman ◽  
...  
Keyword(s):  
Spatial Model ◽  
Initial Phase ◽  
Vegetation Index ◽  
Classification Model ◽  
Linear Regression Method ◽  
Gis Technology ◽  
Vegetative Phase ◽  
Environmental Vulnerability ◽  
Random Forest Classification ◽  
Generative Phase

The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in Indonesia.  Paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35.

scholarly journals Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqin Tong ◽  
Yuhai Bao ◽  
Rigele Te ◽  
Qiyun Ma ◽  
Si Ha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Vegetation Coverage ◽  
Semiarid Region ◽  
Linear Regression Method ◽  
Upward Trend ◽  
Drought Effect ◽  
Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

Rapid earthquake loss assessment based on machine learning and representative sampling

2021 ◽  
pp. 875529302110423
Author(s):  
Zoran Stojadinović ◽  
Miloš Kovačević ◽  
Dejan Marinković ◽  
Božidar Stojadinović
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Local Network ◽  
Loss Assessment ◽  
Damage Classification ◽  
Sampling Algorithm ◽  
Representative Sampling ◽  
Random Forest Classification ◽  
Area Of Interest ◽  
Earthquake Loss

This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.

scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

2020 ◽  
Author(s):  
Carolyn Lou ◽  
Pascal Sati ◽  
Martina Absinta ◽  
Kelly Clark ◽  
Jordan D. Dworkin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
List Type ◽  
Training Set ◽  
Random Forest Classification ◽  
Automated Method ◽  
Forest Classification

AbstractBackground and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.HighlightsA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.

scholarly journals Estimating malaria burden in Nigeria: a geostatistical modelling approach

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Nnadozie Onyiri
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Model ◽  
Vegetation Index ◽  
Malaria Prevalence ◽  
Malaria Risk ◽  
Water Bodies ◽  
Area Index ◽  
Environmental Covariates

This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria’s belt of middle regions generally has malaria prevalence of 40% and above.

scholarly journals The Signature Microbiota Drive Rumen Function Shifts in Goat Kids Introduced to Solid Diet Regimes

2019 ◽  
Vol 7 (11) ◽  
pp. 516 ◽  
Author(s):  
Xiaokang Lv ◽  
Jianmin Chai ◽  
Qiyu Diao ◽  
Wenqin Huang ◽  
Yimin Zhuang ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Volatile Fatty Acids ◽  
Animal Health ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Fatty Acid Production ◽  
Random Forest Classification ◽  
Core Species ◽  
Solid Diet ◽  
Rumen Development

The feeding regime of early, supplementary solid diet improved rumen development and production in goat kids. However, the signature microbiota responsible for linking dietary regimes to rumen function shifts are still unclear. This work analyzed the rumen microbiome and functions affected by an early solid diet regime using a combination of machine learning algorithms. Volatile fatty acids (i.e., acetate, propionate and butyrate) fermented by microbes were found to increase significantly in the supplementary solid diet groups. Predominant genera were found to alter significantly from unclassified Sphingobacteriaceae (non-supplementary group) to Prevotella (supplementary solid diet groups). Random Forest classification model revealed signature microbiota for solid diet that positively correlated with macronutrient intake, and linearly increased with volatile fatty acid production. Bacteria associated with carbohydrate and protein metabolism were also identified. Utilization of a Fish Taco analysis portrayed a set of intersecting core species contributed to rumen function shifts by the solid diet regime. The core community structures consisted of the specific, signature microbiota and the manipulation of their symbiotic partners are manipulated by extra nutrients from concentrate and/or forage, and then produce more volatile fatty acids to promote rumen development and functions eventually host development. Our study provides mechanisms of the microbiome governed by a solid diet regime early in life, and highlights the signature microbiota involved in animal health and production.

scholarly journals Biochemical analysis and assessment of modern monogamous varieties of hemp Cannabis sativa L. by the content of cannabinoid substances

2018 ◽  
Vol 23 ◽  
pp. 321-328
Author(s):  
A. S. Polyakova
Keyword(s):  
Growth And Development ◽  
Initial Phase ◽  
Biochemical Analysis ◽  
Cannabis Sativa ◽  
Practical Importance ◽  
Total Absence ◽  
Cannabidiolic Acid ◽  
Neutral Compounds ◽  
Generative Phase ◽  
Layer Chromatography

Aim. Defining the peculiarities of forming the cannabinoid substances in modern monogamous varieties of hemp is of both theoretical and practical importance. Methods. Modern monogamous varieties of hemp bred in Hlukhiv, Zolotonosha and specimens receive from France; were used for the chromatographically analysis of «damp» and dried in the shadow plant samples was conducted by the method of thin layer chromatography (TLC). Results. It was founding, that in the initial phase of growth and development the Ukrainian hemp varieties with the practical absence of neutral compounds and the French varieties with the sufficient content of these substances formed only natural acids. During the generative phase of growth and development, the varieties from France formed not only natural acids but also neutral compounds. The Ukrainian varieties demonstrated only insufficient increase of natural acids. Conclusions. In the initial phase of growth and development, the investigated hemp varieties containing both large and insignificant amount of cannabinoid substances formed only natural acids. Separate plants demonstrated the decrease of natural acids up to their total absence, which proves their biological interconnections and possible forming cannabidiol, tetrahydrocannabinol and сannabinol by way of fermentative decarboxylation the respective acids. Keywords: Cannabinol (CBN), cannabidiol (CBD), tetrahydrocannabinol (THC), cannabidiolic acid (CBDA), tetrahydrocannabinolic acid (THCA). 

scholarly journals Epigenetic Analyses of Alcohol Consumption in Combustible and Non-Combustible Nicotine Product Users

Epigenomes ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 18
Author(s):  
Kelsey Dawes ◽  
Luke Sampson ◽  
Rachel Reimer ◽  
Shelly Miller ◽  
Robert Philibert ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Self Report ◽  
Classification Model ◽  
Public And Private ◽  
Random Forest Classification ◽  
Epigenetic Biomarkers ◽  
Forest Classification ◽  
Heavy Alcohol Consumption ◽  
Relationship Of ◽  
The Relationship

Alcohol and tobacco use are highly comorbid and exacerbate the associated morbidity and mortality of either substance alone. However, the relationship of alcohol consumption to the various forms of nicotine-containing products is not well understood. To improve this understanding, we examined the relationship of alcohol consumption to nicotine product use using self-report, cotinine, and two epigenetic biomarkers specific for smoking (cg05575921) and drinking (Alcohol T Scores (ATS)) in n = 424 subjects. Cigarette users had significantly higher ATS values than the other groups (p < 2.2 × 10−16). Using the objective biomarkers, the intensity of nicotine and alcohol consumption was correlated in both the cigarette and smokeless users (R = −0.66, p = 3.1 × 10−14; R2 = 0.61, p = 1.97 × 10−4). Building upon this idea, we used the objective nicotine biomarkers and age to build and test a Balanced Random Forest classification model for heavy alcohol consumption (ATS > 2.35). The model performed well with an AUC of 0.962, 89.3% sensitivity, and 85% specificity. We conclude that those who use non-combustible nicotine products drink significantly less than smokers, and cigarette and smokeless users drink more with heavier nicotine use. These findings further highlight the lack of informativeness of self-reported alcohol consumption and suggest given the public and private health burden of alcoholism, further research into whether using non-combustible nicotine products as a mode of treatment for dual users should be considered.

scholarly journals Exploring the role of geospatial technology in disaster management of Batu City: Qualitative analysis using RQDA method

2021 ◽  
Vol 6 (1) ◽  
pp. 80
Author(s):  
Rony Kriswibowo ◽  
Fatwa Ramdani ◽  
Ismiarta Aknuranda
Keyword(s):  
Disaster Management ◽  
Initial Phase ◽  
Information Technologies ◽  
Qualitative Data ◽  
Local Level ◽  
Questionnaire Data ◽  
Gis Technology ◽  
Geospatial Technology ◽  
Role Of Information

Disaster management is important to reduce the risk casualties as well as the environment and economic impacts. One of the stakeholders in disaster management in Indonesia at the local level is the Regional Disaster Management Agency (Badan Pengelolaan Bencana Daerah – BPBD).  In the process of disaster management, there are several phases. Thi initial phase is known as the prevention phase, which is an attempt to eliminate or reduce the possibility of a threat. In this phase, the role of information technology is important to increase the efficiency in disaster management. One of the information technologies that is used widely in disaster management is the Geographic Information System (GIS). This study explores the role of GIS technology employed at BPBD of Batu City in disaster management. The Research Qualitative Data Analysis (RQDA) method is employed based on the questionnaire data. The results show that the role of GIS technology is very limited. The disaster management at the Batu City is mostly done by the traditional method with the manual approach. The role of technology is limited it needs to be improved. Suggestions for increasing the role of technology is to conduct training on the use of GIS for employees. It is expected to increase the role of technology in disaster management.

scholarly journals Keeping it classy: classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach

2020 ◽  
Vol 77 (9) ◽  
pp. 1564-1573
Author(s):  
J. Benjamin Stout ◽  
Mary Conner ◽  
Phaedra Budy ◽  
Peter Mackinnon ◽  
Mark McKinstry
Keyword(s):  
Error Rate ◽  
Classification Model ◽  
Parameter Estimates ◽  
Pit Tag ◽  
Endangered Fish ◽  
Random Forest Classification ◽  
Management Actions ◽  
Live Fish ◽  
Forest Classification ◽  
Input Structure

The ability of passive integrated transponder (PIT) tag data to improve demographic parameter estimates has led to the rapid advancement of PIT tag systems. However, ghost tags create uncertainty about detected tag status (i.e., live fish or ghost tag) when using mobile interrogation systems. We developed a method to differentiate between live fish and ghost tags using a random forest classification model with a novel data input structure based on known fate PIT tag detections in the San Juan River (New Mexico, Colorado, and Utah, USA). We used our model to classify detected tags with an overall error rate of 6.8% (1.6% ghost tags error rate and 21.8% live fish error rate). The important variables for classification were related to distance moved and response to monsoonal flood flows; however, habitat variables did not appear to influence model accuracy. Our results and approach allow the use of mobile detection data with confidence and allow for greater accuracy in movement, distribution, and habitat use studies, potentially helping identify influential management actions that would improve our ability to conserve and recover endangered fish.

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