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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. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date. The developed methodology can potentially improve the process of remediation of polluted groundwater aquifers by simultaneously identifying the number of pollution source locations and release flux histories. The results of the study can be used to assist remediation engineers in developing an appropriate reclamation approach for a polluted aquifer, where knowledge of groundwater pollutant source characteristics is vital. The suggested method will aid in holding polluters accountable, contributing to predictions about the fate and movement of pollution, and developing a strategy for monitoring compliance. When implementing the results from the modeling technique to real-life scenarios of groundwater pollution, the equifinality of the solution cannot be entirely ruled out. The accuracy of the results is governed by assumptions and simplifications made in the model to simulate the aquifer response. Even the model calibration is dependent on the availability of data, which may be noisy and erroneous, resulting in nonunique solutions. [ABSTRACT FROM AUTHOR]

Groundwater pollution is the biggest threat to sustainability of groundwater resources and even more difficult to detect in case of clandestine sources. At the time when pollution is first detected in randomly located sparse wells, very little is known about the pollution sources. Finding the precise locations of clandestine sources of pollution and their release flux history is the biggest challenge and often termed as a problem belonging to the class of environmental forensics. In this study, two linked simulation optimization–based novel techniques are developed to estimate locations and release flux history from clandestine point sources of groundwater pollution. Simulation model is clubbed with optimization solver to determine the locations and release flux histories of groundwater pollution sources by minimizing the residual error between observed and simulated concentration values. Simulated annealing (SA) and particle swarm optimization (PSO) are used as optimization algorithms. A detailed comparative analysis of these two meta-heuristic optimization algorithms in minimizing the residual error is presented in this study. The performance evaluation of both the algorithms in identifying the sources locations and release flux history is carried out for two synthetic cases and a real-life scenario of groundwater pollution in an aquifer in New South Wales, Australia, which has not been attempted in the past. The results of source location identification and release flux history show the selective applicability of each algorithm in solving real-life scenarios of groundwater pollution. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a significant role in economic and social development. As a challenging but indispensable task, STLF has become a hot topic in the field of energy. However, the inadequacy of existing methods lies in their inability to capture accurate input features that are highly related to the output, as the main focus of research has been on improving the accuracy of STLF, while ignoring its stability. Therefore, in this paper, a novel, robust hybrid forecasting system was developed, composed of four modules: (1) data preprocessing, (2) forecasting, (3) optimization, and (4) evaluation. In the data preprocessing module, an effective data preprocessing scheme based on singular spectrum analysis and gray correlation analysis was used to produce a smoother time series and to mine the best input and output structure for the model. An extreme learning machine (ELM) optimized using a multiobjective genetic algorithm (MOGA) that considers both the forecasting accuracy and stability was employed to provide the forecasting. Additionally, a generalized regression neural network (GRNN) was also used in the subsequent module to perform forecasting. Moreover, to further obtain accurate results and to overcome the drawbacks of using single models, a simulated annealing (SA) algorithm was utilized to optimize the combined parameters of the MOGA–ELM and GRNN algorithms in the optimization module. To validate the proposed model, half-hourly load data from the New South Wales and Tasmania are provided as illustrative cases. The experimental results show that the proposed hybrid model obtains more accurate and stable results than the reference models used for comparison. [ABSTRACT FROM AUTHOR]