IPRC - 2019
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/20881
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Item Modeling and Forecasting Selected Climatic Factors Influenced on Sustainable Cultivation Plan: A Case Study for Dompe-Gampaha District(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Hakmanage, N.M.; Chandrasekara, N.V.; Jayasundara, D.D.M.The agriculture is the back born of economy of the most Asian countries. Although the country is moving towards industrialization, the agricultural sector still continues to be an important sector in the economy in Sri Lanka. Cultivation is the predominant sector of the agriculture. Lack of sufficient amount of water is the main limitation factor for cultivation while flood/ deluge is causing the waste of harvest. The main water source for cultivation in Sri Lanka is rainfall. Moreover, for each crop due to its peculiarities and mainly owing to its geographical origin, there exist specific temperature limits within which these plants are able to grow and reproduce. Hence rainfall and temperature are imperative factors influenced on cultivation. More accurate forecasting of monthly rainfall and temperature is significantly important in irrigation schedule, water resources management, crop pattern design and designing of harvesting amount. The main objective of this study is to build suitable forecasting models for two climatic factors: Temperature and Rainfall which affect sustainable cultivation plan. Monthly data of rainfall and temperature from 2009 to 2019 of Dompe-Gampaha district was considered for the study. First 80% of data was used to formulate the models and the rest 20% data was used to validate the models. The paper introduces two fundamentally different approaches for designing a model, the statistical method based on seasonal autoregressive integrated moving average (SARIMA) and decomposed ARIMA model. Mean absolute percentage error (MAPE), Root mean squared error (RMSE) was used to evaluate the performance of fitted models. Among the fitted ten models, SARIMA(0,0,0)(1,0,1)12 was identified as the better model to forecast rainfall based on minimum Akaike information criterion (AIC) where MAPE and RMSE are 48.57% and 5.1339 respectively. Although Box Ljung lack of fit test prove that this model is suitable model, the errors are extremely high. Then decompose ARIMA model was used by calculating seasonal and trend component using SARIMA(0,0,0)(0,1,0)12 and linear regression (Trend=14.33321–0.04884*time) models respectively. Summation of forecasted values of these two models is the forecasted value of decompose ARIMA model and it exhibits MAPE which is 20% lower than the SARIMA(0,0,0)(1,0,1)12 model. Therefore, fitted decomposed ARIMA model can be recommend as a better model to forecast rainfall of Dompe-Gampaha district. Similar approach was carried out to find a suitable model to forecast temperature. SARIMA(1,0,0)(2,0,1)12 was the most accurate model to forecast temperature with minimum AIC value. MAPE and RMSE of this model was 1.3938% and 0.4695 respectively. Lack of fit test and errors provide evidence to say that the fitted SARIMA(1,0,0)(2,0,1)12 is suitable to forecast temperature in the study area. The forecasted values of rainfall and temperature can be used when developing sustainable cultivation plan in Dompe-Gampaha district which leads to development of agricultural sector of the countryItem Prediction of Dengue Incidence Based on Time Series Modelling in the District of Colombo, Sri Lanka(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Udayanga, L.; Herath, K.; Gunanthilaka, N.; Iqbal, M.C.M.; Abeyewickreme, W.Timely implementation of intervention activities, is essential in controlling dengue epidemics. This requires the prediction of dengue epidemics, while respecting the spatial and temporal trends in dengue incidence. However, such aspects are limitedly focused in dengue epidemic management of Sri Lanka. Therefore, the current study was conducted to develop a temporal prediction model for dengue incidence in the district of Colombo in Sri Lanka. Dengue cases reported from 2000 to 2018 in the district of Colombo were collected from the Epidemiology Unit, Sri Lanka. Selected meteorological parameters such as number of rainy days, monthly cumulative rainfall, minimum and maximum relative humidity and temperature corresponding to the same study period were collected from the Department of Meteorology, along with the Oceanic Niño Index (ONI) from the National Oceanic and Administration (NOAA) Centre. All the data were arranged at monthly level. After evaluation of the normality, seasonality, stationarity and seasonal stationarity of the epidemic data, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model was fitted for the prediction of dengue by using the R statistical package. Subsequently, the meteorological factors and the dengue incidence was subjected to a cross correlation analysis to identify the most representative meteorological factors associated with dengue epidemic incidence and an Autoregressive Integrated Moving Average with Exogeneous Input (ARIMAX) model was fitted. The best fitted SARIMA (0, 1, 0) (3, 0, 0)12 model was characterized by an Akaike Information Criteria value (AIC) of -19.04, Bayesian information criterion (BIC) of -5.42, Mean error (ME) of 0.002 and Root Mean Square Error (RMSE) of 0.518. According to the cross correlation analysis, number of rainy days (RD) and Oceanic Niño Index (ONI) denoted a significant negative association with the reported dengue cases in Colombo, while monthly cumulative rainfall (RF), maximum relative humidity (Max_RH), maximum temperature (Max_T) and minimum temperature (Min_T) shared a positive correlation (P < 0.05 at 95% level of confidence). The best fitting ARIMAX model (as indicated below) was characterized by an AIC of -15.74, BIC of -11. 2, ME of 0.006 and RMSE of 0.171. ARIMA (0, 1, 1) + [-0.0006 RDt-3 + 0.0008 RFt-3 + 0.0260 Max_RHt-3 + 0.0766 Min_Tt—4 - 0.0661 ONIt-5] Based on the performance, the ARIMAX model is recommended to be used for the prediction of dengue incidence in the Colombo district to ensure rational allocation of resources for vector control and dengue epidemic managementItem A Time-Series Analysis of the Incidence of Leishmaniasis Integrated with Climatic Variables in Kurunegala District, Sri Lanka(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Wijerathna, T.; Gunathilaka, N.; Gunawardena, K.; Rodrigo, W.Leishmaniasis is one of the main health considerations in the tropical areas of the world. The disease is caused by the parasites of genus Leishmania, which is transmitted from one host to another through female Phlebotomine sand flies. The information on the correlation between weather conditions and leishmaniasis in Sri Lanka is limited. However, studies from other tropical countries suggest that leishmaniasis is highly influenced by climatic variables, but the nature and magnitude of these effects may differ from one geographical region to another. In the current study, we conducted a time series analysis of the number of patients reported from Kurunegala District of Sri Lanka integrating climatic factors as external regressors. Monthly reported cases of lesihmaniasis from January 2014 to December 2018 in Kurunegala District were tracked from the Regional Director of Health Services (RDHS) office. The climatic factors recorded from regional Agro meteorological stations in Kurunegala District were obtained. A time series of the number of patients was created using “tseries” package of R statistical software. The variance of the time series was stabilized by log transformation. The “forecast” package of R software was used to generate an ARIMA model. Resulting model was slightly changed based on the partial autocorrelation function (PACF) plot, the autocorrelation function (ACF) plot, and the number of differences required to achieve the stationarity of the time series. These models were assessed by Akaike information criterion for goodness-of-fit. Spearman’s rank correlation and cross-autocorrelation tests were performed to assess the associations between the number of patients and climatic variables at different lags. The most associated lags of each factor was used as external regressors in a multivariate ARIMA model to assess the effects of climatic factors on the predictive power of the model. The application of “auto.arima” function of forecast package to the log transformed and differenced time series of the number of patients resulted in the model ARIMA (1,1,0), which is also the selected model as it had the lowest AIC among the models generated by changing the values of autoregressive (p), integrative (d), and moving average (q) terms of the model. The time independency of the residual series according to the Ljung–Box test further confirmed the suitability of this model for forecasting. The maximum temperature and the relative humidity were positively correlated with the occurrence of leishmaniasis at 1 and 3 months’ lag periods respectively, which can be plausibly explained by the conditions being favorable for vector sand flies and the climate driven changes in host immunity. However, the integration of climatic factors did not increase the predictive power of the model, indicating the possibility of a latent interaction effect between the climate and the regressing terms (AR and MA) of the model or a stochastic mechanism of interactions between weather factors and leishmaniasis incidence Therefore, the climatic factors, despite their effects on the disease incidence, cannot be used to improve the predictive power of the ARIMA model.