Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region’s characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.

中文翻译：

---

大尺度气候指数和机器学习技术的降水预报

全球变暖是我们这个时代最复杂的挑战之一，给我们的社会和环境造成了相当大的压力。从威胁粮食生产的不断变化的天气模式到加剧灾难性洪水风险的海平面上升，全球变暖的影响以各种方式前所未有。在与全球变暖相关的各个方面，水资源管理越来越受到关注。该领域旨在防止未来威胁人类的水危机。这种管理的第一个阶段是识别影响未来水资源状况的预期气候参数。在这种情况下，迄今为止已经开发和应用了许多预测模型、方法和工具。顺应潮流，目前的研究打算在多层感知器 (MLP) 网络平台上比较三种优化算法，以探索伊朗首都的大规模气候指数 (LSCI) 与降水之间的任何有意义的联系，伊朗是一个位于干旱地区的国家。和半干旱地区，多年来由于管理不善和全球变暖加剧了严重的水资源短缺。这种情况已促使大量人口迁移到国内较发达的城市，尤其是德黑兰。因此，该市当前和未来的环境条件，特别是供水条件非常重要。为解决这一复杂问题，应提供未来降水的展望，并应制定与该地区特征相适应的适当预测轨迹。为此，本研究在 MLP 平台上研究了三种训练方法，即反向传播 (BP)、遗传算法 (GA) 和粒子群优化 (PSO) 算法。本研究指出了两个以输入组成为特征的框架：并发模型框架 (CMF) 和集成模型框架 (IMF)。通过这两个框架，生成了 13 个案例：CMF 中的 12 个案例，每个案例都包含相同提前期中所有选定的 LSCI，而 IMF 中的一个案例由最相关的 LSCI 与每个中德黑兰降水的组合构成交货时间。