相关实验视频

使用深度学习预测北印度具有挑战性的气候条件的温度和降雨量

Syed Nisar Hussain Bukhari¹, Kingsley A Ogudo²

¹National Institute of Electronics and Information Technology (NIELIT), Srinagar, J&K, India.

PeerJ. Computer science

|September 24, 2025

概括

准确的温度和降雨 (T&R) 预测对北印度至关重要. 使用重复神经网络 (RNN) 的深度学习框架在精确,本地化的天气预报方面表现优于长期短期记忆 (LSTM).

科学领域:

气象学和气候学
人工智能的人工智能
数据科学数据科学数据科学

背景情况:

准确的温度和降雨 (T&R) 预报对于北印度等气候敏感地区至关重要.
传统的预测方法与计算需求和本地化实时预测作斗争.
在高效,局部化的T&R预测中存在一个研究缺口,用于天气不稳定的地区.

研究的目的:

开发和评估一个深度学习框架,用于查,克什米尔和拉达克的精确T&R预测.
在局部化的时间序列天气数据中捕捉复杂的时间依赖.
为了比较循环神经网络 (RNN) 和长期短期记忆 (LSTM) 架构的性能.

主要方法:

利用印度气象局 (IMD) 的2000-2023年查,斯里南加和拉达克的数据.
用于时间序列预测的重复神经网络 (RNN) 和长期短期记忆 (LSTM).
在单输入单输出 (SISO) 和多输入多输出 (MIMO) 配置中评估模型.

主要成果:

在SISO设置中,RNN和LSTM都表现出强大的性能.
在捕捉时间关系方面,RNN模型始终优于LSTM.
在MIMO配置中的RNN在所有站点实现了显著较低的MAE,RMSE和MSE.

结论:

该RNN模型在本地化T&R预测方面表现出卓越的精度.
这种深度学习框架为实时天气预测提供了一个实用的工具.
这些发现有助于改善该地区的气候适应,防灾和可持续发展.

关键词:

深度学习是一种深度学习.预测预测预测预测长期短期记忆长期短期记忆降雨量降雨量降雨量经常性的神经网络.温度温度是指温度.

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