scholarly journals An Attempt to Use Non-Linear Regression Modelling Technique in Long-Term Seasonal Rainfall Forecasting for Australian Capital Territory

Geosciences ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 282 ◽  
Author(s):  
Iqbal Hossain ◽  
Rijwana Esha ◽  
Monzur Alam Imteaz
Keyword(s):  
Linear Regression ◽  
Linear Models ◽  
Southern Oscillation ◽  
Seasonal Rainfall ◽  
Modelling Technique ◽  
Australian Capital Territory ◽  
Rainfall Forecasting ◽  
Australian Capital ◽  
Non Linear

The objective of this research is the assessment of the efficiency of a non-linear regression technique in predicting long-term seasonal rainfall. The non-linear models were developed using the lagged (past) values of the climate drivers, which have a significant correlation with rainfall. More specifically, the capabilities of SEIO (South-eastern Indian Ocean) and ENSO (El Nino Southern Oscillation) were assessed in reproducing the rainfall characteristics using the non-linear regression approach. The non-linear models developed were tested using the individual data sets, which were not used during the calibration of the models. The models were assessed using the commonly used statistical parameters, such as Pearson correlations (R), root mean square error (RMSE), mean absolute error (MAE) and index of agreement (d). Three rainfall stations located in the Australian Capital Territory (ACT) were selected as a case study. The analysis suggests that the predictors which has the highest correlation with the predictands do not necessarily produce the least errors in rainfall forecasting. The non-linear regression was able to predict seasonal rainfall with correlation coefficients varying from 0.71 to 0.91. The outcomes of the analysis will help the watershed management authorities to adopt efficient modelling technique by predicting long-term seasonal rainfall.

Long-term seasonal rainfall forecasting: efficiency of linear modelling technique

2018 ◽  
Vol 77 (7) ◽  
Author(s):  
Iqbal Hossain ◽  
H. M. Rasel ◽  
Monzur Alam Imteaz ◽  
Fatemeh Mekanik
Keyword(s):  
Seasonal Rainfall ◽  
Modelling Technique ◽  
Rainfall Forecasting ◽  
Linear Modelling

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

2016 ◽  
Vol 48 (3) ◽  
pp. 867-882 ◽  
Author(s):  
M. S. Babel ◽  
T. A. J. G. Sirisena ◽  
N. Singhrattna
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Large Scale ◽  
Resource Planning ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Atmospheric Variables

Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall in May–June–July (MJJ), August–September–October (ASO), November–December–January (NDJ), and February–March–April (FMA) and to determine the effects of climate change on seasonal rainfall. LSAVs, temperature, pressure, wind, precipitable water, and relative humidity at different lead times were identified as the significant predictors. To determine the impacts of climate change the predictors obtained from two general circulation models, CSIRO Mk3.6 and MPI-ESM-MR, were used with quantile mapping bias correction. Our results show that the models with the best performance for FMA and MJJ seasons are able to forecast rainfall one month in advance for these seasons and the best models for ASO and NDJ seasons are able do so two months in advance. Under the RCP4.5 scenario, a decreasing trend of MJJ rainfall and an increasing trend of ASO rainfall can be observed from 2011 to 2040. For the dry season, while NDJ rainfall decreases, FMA rainfall increases for the same period of time.

Single institutional series of neuroendocrine tumours managed in the Australian capital territory.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e14703-e14703
Author(s):  
Laeeq Malik ◽  
Yu Jo Chua ◽  
Nadeem Butt ◽  
Desmond Yip
Keyword(s):  
Neuroendocrine Tumours ◽  
Histological Grade ◽  
Tumour Size ◽  
Primary Tumour ◽  
Limited Information ◽  
Australian Capital Territory ◽  
Long Term Outcome ◽  
Male Predominance ◽  
Australian Capital

e14703 Background: Neuroendocrine tumours (NETs) have been regarded as indolent tumors with significantly variable clinical behavior. Limited information is available on long-term clinical outcome and clinically applicable prognostic factors.We performed a retrospective review of NETS managed in the Australian Capital Territory (ACT) over a 12-year period,with examination of epidemiology and various prognostic clinicopathologic factors. Methods: This multicenter analysis included patients in ACT and surrounding New South Wales treated with histologically proven neuroendocrine tumor (lung carcinoids excluded). The cases were identified from hospital databases. Data was analysed according to epidemiological, clinical and histopathological characteristics. Results: The cohort of 107 patients showed slight male predominance. Median age at diagnosis was 62 years and tumour size of 1.2 cm. The most common primary tumour site was jejunum/ileum (32%) followed by rectum (22%) and pancreas (11.2 %). Most patients had localised disease at initial diagnosis (n- 73/107 (68%). Distant metastases were seen in 32% (n-34/107) on initial staging with liver being most common site. Most patients were symptomatic at diagnosis while 22.4% cases were found incidentally. Second malignancies in particular of gastrointestinal origin were diagnosed in 33.6% (n-36/107). Surgical debulking was the most common treatment (59.8%) while 18% had multi-modality therapy. At a median followup of 25 months from diagnosis, 76 patients (78%) were still alive. Median time to first relapse was 15 months. 5 year survival rate was 75% for NETs originating from jejunum/ilieum on Kaplan-Meier analysis. Increasing age, tumor size, male gender, high histological grade, high Ki 67 index, raised plasma chromogranin A and urine 5 HIAA at the time of diagnosis were associated with shorter 5-year survival. Conclusions: The epidemiologic characteristics and long-term outcome in our series was comparable to reported studies from other centers. This analysis confirmed some important prognostic factors that could be considered for risk stratification and therapeutic management in patients with NETs.

Demographic and climatic factors associated with dengue prevalence in a hyperendemic zone in Mexico: an empirical approach

2020 ◽  
Author(s):  
Francisco Espinoza-Gomez ◽  
Oscar Alberto Newton-Sanchez ◽  
Arnulfo Hernan Nava-Zavala ◽  
Maria G Zavala-Cerna ◽  
Fabian Rojas-Larios ◽  
...  
Keyword(s):  
Linear Models ◽  
Climatic Factors ◽  
Economic Status ◽  
Real Life ◽  
Climate Variables ◽  
Mexican Pacific ◽  
Non Linear ◽  
Main Determinants ◽  
Socio Economic Factors

Abstract Background Many models for predicting dengue epidemics use incidence and short-term changes in climate variables, however, studies in real-life scenarios for correlations of seroprevalence (SP) with long-term climate variables and with integration of socio-economic factors are scarce. Our objective was to analyse the combined correlation between socio-economic and climate variables with the SP of dengue in Mexico. Methods We performed a seroepidemiological ecological study on the Mexican Pacific coast. Dengue SP was estimated by the presence of immunoglobulin G antibodies in 1278 inhabitants. We implemented multiple correlations with socio-economic, climatic and topographic characteristics using logistic regression, generalized linear models and non-linear regressions. Results Dengue SP was 58%. The age-adjusted correlation was positive with the male sex, while a negative correlation was seen with socio-economic status (SES) and scholl level (SL). The annual temperature showed a positive correlation, while the altitude was negative. It should be noted that these correlations showed a marked ‘S’ shape in the non-linear model, suggesting three clearly defined scenarios for dengue risk. Conclusion Low SES and SL showed an unexpected paradoxical protective effect. Altitude above sea level and annual temperature are the main determinants for dengue in the long term. The identification of three clearly delineated scenarios for transmission could improve the accuracy of predictive models.

More Linear Models in R!

2021 ◽  
pp. 139-180
Author(s):  
Justin C. Touchon
Keyword(s):  
Linear Regression ◽  
Linear Model ◽  
Confidence Intervals ◽  
Linear Models ◽  
Analysis Of Covariance ◽  
Regression Curves ◽  
The World ◽  
Non Linear ◽  
Post Hoc

Chapter 6 continues exploring the world of statistics that are covered within the linear model, namely two-way and three-way ANOVA, linear regression and analysis of covariance (ANCOVA). In each type of model, a detailed description of how to interpret the summary output is undertaken, including understanding how to interpret and plot interactions. Conducting post-hoc analyses and using the predict() function are also covered. The chapter ends by reinforcing earlier plotting skills in ggplot2 by walking through an example of making a professional looking figure with multiple non-linear regression curves and confidence intervals.

scholarly journals Predictors of Tropical Cyclone Numbers and Extreme Hurricane Intensities over the North Atlantic Using Generalized Additive and Linear Models

2009 ◽  
Vol 22 (3) ◽  
pp. 633-648 ◽  
Author(s):  
Olivier Mestre ◽  
Stéphane Hallegatte
Keyword(s):  
Tropical Cyclones ◽  
North Atlantic ◽  
Linear Models ◽  
Southern Oscillation ◽  
Additive Models ◽  
Annual Number ◽  
The North ◽  
The North Atlantic ◽  
Energy Dissipated

Abstract Fluctuations of the annual number of tropical cyclones over the North Atlantic and of the energy dissipated by the most intense hurricane of a season are related to a variety of predictors [global temperature, SST and detrended SST, North Atlantic Oscillation (NAO), Southern Oscillation index (SOI)] using generalized additive and linear models. This study demonstrates that SST and SOI are predictors of interest. The SST is found to influence positively the annual number of tropical cyclones and the intensity of the most intense hurricanes. The use of specific additive models reveals nonlinearity in the responses to SOI that has to be taken into account using changepoint models. The long-term trend in SST is found to influence the annual number of tropical cyclones but does not add information for the prediction of the most intense hurricane intensity.

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