Gene Expression Programming and Machine Learning Methods for Bushfire Susceptibility Mapping in New South Wales, Australia

2021 ◽  
Author(s):  
Maryamsadat Hosseini ◽  
Samsung Lim
Keyword(s):  
Gene Expression ◽  
New South ◽  
Gene Expression Programming ◽  
Single Gene ◽  
New South Wales ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Techniques ◽  
South Wales ◽  
Susceptibility Maps

Abstract Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known statistical methods to generate bushfire susceptibility maps for New South Wales, Australia as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and LR and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.890), while RFFR had the highest accuracy (94.70%) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. The ensemble methods had better performances than those of the single methods.

scholarly journals Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Yang ◽  
Yao Dong ◽  
Yanhua Chen ◽  
Caihong Li
Keyword(s):  
New South ◽  
Electrical Power ◽  
New South Wales ◽  
Garch Model ◽  
Electricity Price ◽  
Support Vector ◽  
Ant Colony Optimization Algorithm ◽  
Electrical Power System ◽  
Combined Model ◽  
South Wales

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.

scholarly journals Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 585 ◽  
Author(s):  
Abhirup Dikshit ◽  
Biswajeet Pradhan ◽  
Abdullah M. Alamri
Keyword(s):  
Drought Index ◽  
New South ◽  
New South Wales ◽  
Temporal Trends ◽  
Mitigation Strategies ◽  
Sea Surface ◽  
Dominant Factor ◽  
Economic Losses ◽  
Support Vector ◽  
South Wales

Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R2 value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region.

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