神经网络是可靠的预测器吗？一个MARS查询！
Is Neural Network a Reliable Forecaster on Earth? A MARS Query!
摘要：
长期降雨预测是一项具有挑战性的任务，特别是在现代世界，我们正面临着全球变暖的主要环境问题。一般来说，气候和降雨是自然界中高度非线性的现象，表现出所谓的蝴蝶效应。虽然世界上一些地区注意到年降雨量有系统地减少，但其他地区注意到洪水和严重风暴的增加。这一现象的全球性质非常复杂，需要复杂的计算机建模和模拟来准确预测。在本文中，我们报告了一个多变量自适应回归样条（MARS）和人工神经网络的性能分析，提前一个月的降水预测。为了评估预报效率，我们利用了位于印度半岛南部的喀拉拉邦87年的降水资料，该地区位于纬度-经度对（8 o29 'N-76 o 57' E）。我们使用了一个人工神经网络训练使用缩放共轭梯度算法。神经网络和MARS的训练与40年的降雨量数据。对于性能评估，将网络预测输出与实际降雨数据进行比较。仿真结果表明，MARS是一个很好的预测工具，表现优于所考虑的神经网络。
英文摘要：
Long-term rainfall prediction is a challenging task especially in the modern world where we are facing the major environmental problem of global warming. In general, climate and rainfall are highly non-linear phenomena in nature exhibiting what is known as the butterfly effect. While some regions of the world are noticing a systematic decrease in annual rainfall, others notice increases in flooding and severe storms. The global nature of this phenomenon is very complicated and requires sophisticated computer modeling and simulation to predict accurately. In this paper, we report a performance analysis for Multivariate Adaptive Regression Splines (MARS)and artificial neural networks for one month ahead prediction of rainfall. To evaluate the prediction efficiency, we made use of 87 years of rainfall data in Kerala state, the southern part of the Indian peninsula situated at latitude -longitude pairs (8o29'N - 76o57' E). We used an artificial neural network trained using the scaled conjugate gradient algorithm. The neural network and MARS were trained with 40 years of rainfall data. For performance evaluation, network predicted outputs were compared with the actual rainfall data. Simulation results reveal that MARS is a good forecasting tool and performed better than the considered neural network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
