Abstract
Predicting the impact of climate change on water availability of many regions, watersheds, or countries is currently a popular subject of research given the different signs of changes in climate. A thorough search of the relevant literature revealed that mainly conceptual, statistical, or stochastic models are used for the prediction of climatic impacts on water availability. In some studies, nature-based algorithms such as neural networks or genetic algorithms were used in predictions. From the performance metrics and according to the authors of such studies, the accuracy of nature-based models is much more consistent than the conceptual or statistical models. That is why the present study tries to analyze and compare the ability of nature-based models to predict climatic impacts on water availability from a data set that represents all possible combinations of input and output variables. Ultimately the goal is to identify the best nature-based model from among the many available ones. The investigation was able to use only four of the most popular nature-based algorithms namely Artificial Neural Network, Genetic Algorithm, Ant Colony Optimization and Artificial Bee Colony algorithm. Readers can employ other meta-heuristics to compare the performance of all such algorithms in prediction of climatic impact on water resources.
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Majumder, M., Barman, R.N. (2013). Comparison of Nature-Based Algorithms in Impact Analysis of Climate Change on Water Resources. In: Majumder, M., Barman, R. (eds) Application of Nature Based Algorithm in Natural Resource Management. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5152-1_6
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DOI: https://doi.org/10.1007/978-94-007-5152-1_6
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