scholarly journals Incidence forecasting of new invasive pest of coconut rugose spiraling whitefly (Aleurodicus rugioperculatus) in India using ARIMAX analysis

2021 ◽  
Vol 23 (2) ◽  
pp. 194-199
Author(s):  
K.ELANGO ◽  
S. JEYARAJAN NELSON ◽  
P.DINESHKUMAR
Keyword(s):  
Tamil Nadu ◽  
Prediction Models ◽  
Time Series Data ◽  
Management Strategies ◽  
Climatic Conditions ◽  
Invasive Pest ◽  
Series Data ◽  
Maximum Temperature ◽  
Weather Factors ◽  
Extent Of Damage

The rugose spiraling whitefly (RSW), Aleurodicus rugioperculatus Martin is a new invasive pest occurring in several crops including coconut since 2016 in India from Tamil Nadu, Karnataka, Kerala and Andhra Pradesh. The population dynamics of new invasive whitefly species, A. rugioperculatus study indicated that RSW was found throughout the year on coconut and the observation recorded on weekly interval basis shows that A. rugioperculatus population escalated from the first week of July 2018 (130.8 nymph/ leaf/ frond) reaching the maximum during the first week of October (161.0 nymph/ leaf/ frond) which subsequently dwindled to a minimum during April. Due to variation in the agro-climatic conditions of different regions, arthropods show varying trends in their incidence also in nature and extent of damage to the crop. Influence of weather parameters on rugose spiralling whitefly incidence is lacking, which is essential for developing management strategies. The forecasting model to predict rugose spiralling whitefly incidence in coconut was developed by ARIMAX model of weekly cases and weather factors. In exploring different prediction models by fitting covariates to the time series data, ARIMA (0,2,1) with Maximum temperature was found best model for predicting the rugose  spiralling whitefly incidence and all covariates were found non-significant predictors except maximum temperature.

scholarly journals Population dynamics of exotic rugose spiralling whitefly, Aleurodicus rugioperculatus Martin (Hemiptera: Aleyrodidae) on coconut as influenced by weather factors and natural enemies

2020 ◽  
pp. 120-125
Author(s):  
K. Elango ◽  
S. Jeyarajan Nelson
Keyword(s):  
Population Dynamics ◽  
Negative Correlation ◽  
Natural Enemies ◽  
Tamil Nadu ◽  
Abiotic Factors ◽  
Climatic Conditions ◽  
Weekly Interval ◽  
Maximum Temperature ◽  
Weather Factors ◽  
Extent Of Damage

The rugose spiralling whitefly, Aleurodicus rugioperculatus Martin is a new exotic pest occurring in several crops including coconut since 2016 in India. Due to variation in the agro-climatic conditions of different regions, arthropods show varying trends in their incidence also in nature and extent of damage to the crop. Besides, abiotic factors also play a key role in determining the incidence and dominance of a particular pest and their natural enemies in a crop ecosystem. The population dynamics of new exotic whitefly species, A. rugioperculatus and their associated natural enemies was assessed on five-year-old Chowghat Orange Dwarf coconut trees at Coconut Farm of Tamil Nadu Agricultural University. The study indicated that RSW was found throughout the year on coconut and the observation recorded on weekly interval basis shows that A. rugioperculatus population escalated from the first week of July 2018 (130.8 nymphs/leaf/frond) reaching the maximum during the first week of October (161.0 nymphs/leaf/frond) which subsequently dwindled to a minimum during April. The parasitisation by E. guadeloupae on RSW ranged from 31.60 percent in Aug. 2018 to 57.60 percent in December 2018. The association of biotic and abiotic factors with A. rugioperculatus population showed a negative correlation with E. guadeloupae and C. montrouzieri. There was a significant positive correlation between maximum temperature and minimum temperature as well as relative humidity. However, rainfall showed a negative correlation with A. rugioperculatus population.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

scholarly journals Parameter uncertainty of a dynamic multispecies size spectrum model

2016 ◽  
Vol 73 (4) ◽  
pp. 589-597 ◽  
Author(s):  
Michael A. Spence ◽  
Paul G. Blackwell ◽  
Julia L. Blanchard
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Size Structure ◽  
Management Strategies ◽  
Optimization Methods ◽  
Series Data ◽  
Size Spectrum ◽  
Aquatic Communities ◽  
The North ◽  
Spectrum Model

Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to “calibrate” the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967–2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population- and community-level indicators under alternative management strategies.

scholarly journals Pre-harvest forecast of pigeon-pea yield using discriminant function analysis of weather variables

MAUSAM ◽  
2021 ◽  
Vol 67 (3) ◽  
pp. 577-582
Author(s):  
R. R. YADAV ◽  
B. V. S. SISODIA ◽  
SUNIL KUMAR
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Statistical Models ◽  
Time Series Data ◽  
Function Analysis ◽  
Uttar Pradesh ◽  
Pigeon Pea ◽  
Series Data ◽  
Maximum Temperature ◽  
Weather Variables

In the present paper, an application of discriminant function analysis of weather variables (minimum & maximum temperature, Rainfall, Rainy days, Relative humidity 7 hr & 14 hr, Sunshine hour and Wind velocity )for developing suitable statistical models to forecast pigeon-pea yield in Faizabad district of Eastern Uttar Pradesh has been demonstrated. Time series data on pigeon-pea yield for 22 years (1990-91 to 2011-12) have been divided into three groups, viz., congenial, normal, and adverse based on de-trended yield distribution. Considering these groups as three populations, discriminant function analysis using weekly data on eight weather variables in different forms has been carried out. The sets of discriminant scores obtained from such analysis have been used as regressor variables along with time trend variable and pigeon-pea yield as regressand in development of statistical models. In all nine models have been developed. The forecast yield of pigeon-pea have been obtained from these models for the year 2009-10, 2010-11 and 2011-12, which were not included in the development of the models. The model 4 and 9 have been found to be most appropriate on the basis of R2adj, percent deviation of forecast, percent root mean square error (%RMSE) and percent standard error (PSE) for the reliable forecast of pigeon-pea yield about two and half months before the crop harvest.

scholarly journals Trend Analysis of Meteorological Variables: Rainfall and Temperature

2021 ◽  
Vol 7 (11) ◽  
pp. 1868-1879
Author(s):  
Jada El Kasri ◽  
Abdelaziz Lahmili ◽  
Halima Soussi ◽  
Imane Jaouda ◽  
Maha Bentaher
Keyword(s):  
Water Resources ◽  
Flash Flood ◽  
Groundwater Resources ◽  
Climatic Conditions ◽  
Annual Rainfall ◽  
Series Data ◽  
Maximum Temperature ◽  
Data Set ◽  
Monotonic Trend ◽  
Increasing Trend

The Souss-Massa region in southwestern Morocco is characterized by a semi-arid climate with high variability in rainfall. Frequent droughts and flash flood events combined with overexploitation of water resources in recent decades have had a significant impact on the human security and the economy which is mainly based on agriculture, tourism and fishery. For better management of extreme events and water resources under changing climatic conditions, a study was carried out to quantify the seasonal and annual variability and trends in rainfall and temperature over the past three decades with data from three stations. Climatological representative of the Souss-Massa region. The Mann-Kendall (MK) non-parametric test and the Sen’s slope are used to estimate the monotonic trend and magnitude of the trend of the variables, respectively. Statistical analysis of the rainfall series data set highlights that the occurrence of rainfall is unpredictable and irregular and the both the seasonal and annual rainfall trend appears negative (downward) for all the three climatological stations. The minimum temperature shows a remarkable increasing trend both on annual and seasonal scale while the maximum temperature registers a slightly increasing trend. The study presents some new insights on rainfall and temperature trends that will have significant impacts on the surface and groundwater resources of the region under changing climatic conditions. The results can help to prioritize new strategies to mitigate the risk of droughts, of floods and to manage water resources to sustain the dependence of agriculture tourism and fishery sectors in the region. Doi: 10.28991/cej-2021-03091765 Full Text: PDF

scholarly journals The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 299
Author(s):  
Jianguo Zheng ◽  
Yilin Wang ◽  
Shihan Li ◽  
Hancong Chen
Keyword(s):  
Stock Market ◽  
Prediction Models ◽  
Time Series Data ◽  
Bat Algorithm ◽  
Stock Index ◽  
Stationary Time Series ◽  
Series Data ◽  
Polynomial Kernel ◽  
Chinese Stock Market ◽  
Ann Model

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

scholarly journals Examination of Temperature Variability over Lahore (Pakistan) and Dhaka (Bangladesh): A Comparative Study

2020 ◽  
Vol 10 (4) ◽  
pp. 46-50
Author(s):  
Khadija Shakrullah ◽  
Safdar Ali Shirazi ◽  
Sajjad Hussain Sajjad ◽  
Zartab Jahan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Temperature Variability ◽  
Series Data ◽  
Maximum Temperature ◽  
Average Model ◽  
Temperature Variations ◽  
Moving Average Model ◽  
Mega Cities

Lahore and Dhaka are rapid expanding and over populated cities of South Asia located in Pakistan andBangladesh respectively. The present study focuses on the evaluation of temperature variability in comparison of bothcities. This study primarily aims at the assessment and examination of temperature variations in both mega cities ofSouth Asia which are seasonal as well as the annual. The time series data were analysed by using statistical techniquesAutoregressive Moving Average Model (ARMA) and Autoregressive Integrated Average Model (ARIMA). The resultsreveal that the minimum temperature is increasing much faster than that of the maximum temperature of both cities.However, the temperature rise(in maximum and minimum) has been observed highest during the spring seasons in bothcities.

scholarly journals Future trends of COVID-19 disease outbreak in different states in India: a periodic regression analysis

2020 ◽  
Author(s):  
Bharath Prasad Cholanayakanahalli Thyagaraju ◽  
Srikantha Gowda ◽  
Sharanagouda Patil ◽  
Chandrashekar Srikantiah ◽  
Kuralayanapalya Puttahonnappa Suresh
Keyword(s):  
Tamil Nadu ◽  
Time Series Data ◽  
Human Life ◽  
Disease Outbreak ◽  
Series Data ◽  
Indian Government ◽  
Economic Wealth ◽  
The Future ◽  
Periodic Regression ◽  
The Impact

COVID-19 (Coronavirus disease 19) is the deadliest pandemic, and by August 2, >18.2 million population worldwide were infected with SARS-CoV-2 virus causing burden on human life and economic loss. Disease outbreak analysis has become a priority for the Indian government to initiate necessary healthcare measures in lowering the impact of this deadly pandemic viral disease. In this study, time series data for COVID-19 disease was extracted from the website www.covid19india.org, analysed by using periodic regression model, the expected number of cases till 02 October 2020 was predicted and to develop a stochastic models using periodic regression in the top 15 highly infected states in India. The analysis reported increasing pattern at initial days of prediction and showed a decreasing trend for the number of reporting cases, which may reduce in future days for states like West Bengal, Karnataka, Uttar Pradesh, Bihar, Telangana, Assam and Odisha. However, for the states of Maharashtra, Tamil Nadu, Gujarat, Rajasthan, Haryana and Madhya Pradesh, showed a rapid phase of increase in disease outbreak that is likely to infect more population and indicates the pandemic nature of this disease over a period. Presently, Delhi shows a drastic reduction in the number of cases, that may increase in the future, which can be controlled if appropriate preventive measures are followed strictly and effectively. Our model highlights that continuous and constant efforts are needed for the prevention of new infections of the disease in all states that helps to effectively mitigate the disease and to allocate scarce resources effectively in the future that could improve the economic wealth in India.

scholarly journals Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 99
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Time Scale ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Economic Recession ◽  
Series Data ◽  
Economic Activities ◽  
Novel Approach

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

scholarly journals Analysis of a long-term Forest Accountancy Network to Support Management and Controlling

2018 ◽  
Vol 3 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Markus Dög ◽  
Johannes Wildberg ◽  
Bernhard Möhring
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Management Strategies ◽  
Series Data ◽  
Forest Economics ◽  
Private Forest ◽  
Forest Landowners ◽  
Forest Enterprises

Abstract Multifunctional forestry in Germany is characterized by long production periods and complex biological-technical processes. Private forest enterprises are complex systems which are closely interwoven with the economic environment. To ensure their economic success, forest landowners need to take the economic development into consideration and adapt their management strategies. Management accounting is an important source for information needed to fulfil main tasks of accounting that help to manage forest enterprises: ‘description’, ‘explanation’ and ‘decision making’. To get general data, long time series data, taken from Forest Accountancy Networks (FAN), can be analysed. For more than 45 years, data from the FAN Westfalen-Lippe in Germany has been collected and analysed by the department of Forest Economics and Forest Management at the University of Göttingen. The long-term development and adaptation strategies of defined groups of private forest enterprises can be illustrated using this data. These valuable time series can support decision-making processes for private forest landowners and provide tools for forest policy. The data shows that private forest enterprises, with spruce as the dominating tree species, have performed above average in terms of operating revenues and profit margins, but are also more susceptible to calamities resulting in higher involuntary timber harvests.

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