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 Spatial Analysis of Monthly and Seasonal Rainfall in Sri Lanka(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Kaveendri, D.H.D.; Chandrasekara, N.V.; Arunashantha, H.A.S.Climate change is a critical factor that effect on the variability of environment in many countries globally. Sri Lanka being a country where agricultural sector contributes to the highest proportion of its economy, it is very crucial to identify climatic changes in the growth of the sector. Water plays a major role in agriculture as Sri Lanka face to climatic changes over the time. It is important to manage water resource and changes in precipitation should be identified as a solution for the climatic changes in Sri Lanka. In this study, dense and homogeneous monthly rainfall data over a 10 years period from 2009 to 2018 were considered which is comprised of main 22 rain-gauge stations in Sri Lanka. The main objectives of this study are examining the existence of trends in monthly and seasonal distribution, identifying regional precipitation differences by the spatial interpolation of detected monthly trends and finding the most suitable interpolation technique out of six interpolation methods that were identified from the previous literature. Trend analysis was done by using Mann Kendell test which is non-parametric statistical test and ArcGIS 10.1 software was used for spatial interpolation in geostatistical techniques. Global Polynomial Interpolation, Local Polynomial Interpolation, Inverse Direct Method, Ordinary Kriging, Universal Kriging, Complete Regularized Spline interpolation methods were used to examine the changes in magnitude of unmeasured areas using monthly rainfall data. Root mean squared error (RMSE) value in cross validation is used to compare the identified interpolation techniques. Results exhibits that the positive trends are only shown during the months February, April, May and October which indicate that they are not prominent. Seven out of twelve months show a significant negative trend for 19 stations. For seasonal analysis southwest monsoon shows both positive and negative significant trends while first inter monsoon and Northeast monsoon indicate a negative trend. In conclusion, there were no prominent trends identified in both seasonal and monthly analysis and Kriging method was identified as the optimal algorithm with a minimum RMSE value for monthly spatial interpolation