Prediction of monthly rainfall in Victoria, Australia : Clusterwise linear regression approach
- Bagirov, Adil, Mahmood, Arshad, Barton, Andrew
- Authors: Bagirov, Adil , Mahmood, Arshad , Barton, Andrew
- Date: 2017
- Type: Text , Journal article
- Relation: Atmospheric Research Vol. 188, no. (2017), p. 20-29
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889–2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations. © 2017 Elsevier B.V.
Rainfall prediction in Australia : Clusterwise linear regression approach
- Authors: Mahmood, Arshad
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
- Description: Doctor of Philosophy
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbors methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
- Authors: Mahmood, Arshad
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
- Description: Doctor of Philosophy
- Description: Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbors methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
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