Fahad Shah, Su Fang, Khan Sufyan Ullah, Naeem Muhammad Rashid, Wei Kailei
School of Management, Hainan University, Haikou 570228, Hainan Province, China.
School of Economics and Management, Northwest University, Xi'an, China.
Sci Total Environ. 2023 Jan 1;854:158760. doi: 10.1016/j.scitotenv.2022.158760. Epub 2022 Sep 13.
Variations in rainfall negatively affect crop productivity and impose severe climatic conditions in developing regions. Studies that focus on climatic variations such as variability in rainfall and temperature are vital, particularly in predominant rainfed areas. Forecasting rainfall is very essential in the agriculture sector due to the dependence of many people, while it is very complex to accurately predict rainfall due to its dynamic nature. This study aims to present a deep forecasting model based on optimized (Gated Recurrent Unit) GRU neural network to predict rainfall in Pakistan based on the 30 years of climate data from 1991 to 2020. The climatic variables were first extracted and then fine-tuned by eliminating outliers and extreme values from the data set for precise forecasting. Data normalization strategies were further utilized to adjust numeric values into a standard scale without distorting divergences or losing useful information. The proposed model achieved high prediction accuracy by maintaining minimal Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Squared Error (NRMSE) compared to state-of-the-art rainfall forecasting models. Climatic variables used in the forecasting were evaluated in terms of correlation and regression analysis. The correlation results showed that temperature has a negative association and air quality variables have a positive association with rainfall in each quarter of the year. The second and third quarters of the year showed a high association with rainfall, whereas the air quality variables showed a lesser or no association with rainfall during the first and second quarters of the year. The results further showed a strong association of climatic variables with rainfall for all months of the year. The minimal loss achieved by the proposed model also demonstrated the feasibility of selected variables in precise forecasting of rainfall regardless of volatile climatic conditions.
降雨变化对作物生产力产生负面影响，并给发展中地区带来严峻的气候条件。关注降雨和温度等气候变化的研究至关重要，特别是在主要的雨养农业地区。由于许多人的生计依赖农业，因此降雨预测在农业部门非常重要，然而，由于降雨具有动态特性，准确预测降雨非常复杂。本研究旨在提出一种基于优化门控循环单元（GRU）神经网络的深度预测模型，以根据1991年至2020年的30年气候数据预测巴基斯坦的降雨情况。首先提取气候变量，然后通过消除数据集中的异常值和极值对其进行微调，以实现精确预测。进一步采用数据归一化策略，将数值调整到标准尺度，而不会扭曲差异或丢失有用信息。与现有降雨预测模型相比，所提出的模型通过保持最小的归一化平均绝对误差（NMAE）和归一化均方根误差（NRMSE），实现了较高的预测精度。对预测中使用的气候变量进行了相关性和回归分析评估。相关结果表明，温度与降雨呈负相关，空气质量变量与一年中每个季度的降雨呈正相关。一年中的第二和第三季度与降雨的相关性较高，而空气质量变量在一年的第一和第二季度与降雨的相关性较小或没有相关性。结果还表明，气候变量与一年中所有月份的降雨都有很强的相关性。所提出的模型实现的最小损失也证明了所选变量在精确预测降雨方面的可行性，无论气候条件如何变化。
