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Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach
Advances in Meteorology ( IF 2.9 ) Pub Date : 2021-05-06 , DOI: 10.1155/2021/5524611
Muhammed E. Akiner 1
Affiliation  

Estimating models are becoming increasingly crucial in highlighting the nonlinear connections of the massive level of rough information and chaotic components. The study demonstrates a modern approach utilizing a created artificial neural network (ANN) method that may be an alternative strategy to conventional factual procedures for advancing rainfall estimate execution. A case study was presented for Turkey’s Düzce and Bolu neighboring territories located on the Black Sea’s southern coast. This study’s primary aim is to create an ANN model unique in the field to generate satisfactory results even with limited data. The proposed technique is being used to estimate rainfall and make predictions regarding future precipitation. Bolu daily average rainfall by month data and a limited number of Düzce rainfall data were used. Missing forecasts and potential rainfall projections will be examined in the fundamental research. This research further focuses on ANN computational concepts and develops a neural network for rainfall time series forecasting. The emphasis of this study was a feed-forward backpropagation network. The Levenberg–Marquardt algorithm (LMA) was implemented for training a two-layer feed-forward ANN for the missing rainfall data prediction part of this research. The inaccessible rainfall parameters for Düzce were determined for the years 1995 to 2009. From 2010 to 2020, a two-layer feed-forward ANN was trained using the gradient descent algorithm to forecast daily average rainfall data by month. The findings reported in this study guide researchers interested in implementing the ANN forecast model for an extended period of missing rainfall data.

中文翻译:

利用人工神经网络方法利用受约束的观测量进行长期降雨信息预报

在强调大量粗糙信息和混沌成分之间的非线性联系时,估计模型变得越来越重要。这项研究展示了一种利用创建的人工神经网络(ANN)方法的现代方法,该方法可能是传统事实程序的替代策略,以提高降雨量估计的执行力。提出了一个案例研究,涉及土耳其在黑海南部海岸的杜兹切和博卢周边地区。这项研究的主要目的是创建一个在现场独一无二的ANN模型,即使在数据有限的情况下也能产生令人满意的结果。拟议中的技术被用于估计降雨量并做出有关未来降水的预测。使用了Bolu每个月的日平均降雨量数据和Düzce有限的降雨量数据。基础研究将研究缺少的预测和潜在的降雨预测。这项研究进一步侧重于人工神经网络的计算概念,并开发了用于降雨时间序列预测的神经网络。这项研究的重点是前馈反向传播网络。实施Levenberg-Marquardt算法(LMA)来训练两层前馈ANN,以解决本研究中缺少的降雨数据预测部分。在1995年至2009年期间确定了Düzce不可访问的降雨参数。从2010年至2020年,使用梯度下降算法对两层前馈ANN进行了训练,以按月预测每日平均降雨量数据。这项研究报告的发现指导研究人员有兴趣在长期缺少降雨数据的情况下实施ANN预测模型。
更新日期:2021-05-06
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