Abstract
Several climatic variables like precipitation, temperature, fog, dew, humidity, and wind significantly impact agricultural production. While formulating policies for agriculture and other industrial sectors, precise knowledge regarding climatic variables is essential and helpful. The Bureau of Meteorology (BOM) monitored various climatic variables at more than 226 meteorological stations in New South Wales (NSW), Australia. However, the placement of these monitoring stations was not systematic. As a result, predictions for unobserved sites turn out to be erroneous. Inadequate or poorly placed meteorological stations can lead to inaccurate weather forecasts, make it difficult to fully understand local and regional weather patterns, and, lastly, make it impossible to identify the early warnings for severe weather events like hurricanes, tornadoes, and flash floods. Therefore, the study aims to optimize and suggest a monitoring network to minimize the prediction error of these climatic variables. The optimized monitoring network can be found by optimally adding new meteorological monitoring stations or withdrawing existing ones while still ensuring the reliability of weather data. In this study, the meteorological monitoring network of NSW, Australia was optimized using two stochastic search algorithms: Spatial Simulated Annealing (SSA) and Genetic Algorithms (GA). The Average Kriging Variance (AKV) is considered an accuracy measure for SSA and GA. Ordinary kriging (OK) and Universal Kriging (UK), two popular prediction methods, are used using covariation modeled by the Matheron variogram model. The results reveal that the time consumption for SSA and GA are relatively similar, but the SSA utilizing the UK gives a lower AKV than GA. The optimized meteorological monitoring will be useful for ensuring accurate weather forecasts, providing early warnings of severe weather events, and enabling scientific research to understand and mitigate the effects of climate change. Furthermore, this novel optimization strategy will not only be helpful for the government of Australia but will significantly improve prediction accuracy, providing more reliable information for various weather-related activities, such as agriculture, construction, and emergency management.