Evaluation of ARIMA, Random Forest, and Cubic Spline Models in Predicting the VCI Index in Basra Province, Iraq
Subject Areas :
1 -
Keywords: VCI Index, Random Forest Algorithm, Cubic Spline, ARIMA, Time Series Analysis, Basra Province, Iraq.,
Abstract :
One of the most significant challenges humanity faces is climate change and its resulting consequences, such as drought. Various indices are used to monitor drought, among which the Vegetation Condition Index (VCI) stands out. A crucial issue in this regard is the lack of attention to the accuracy of methods used for predicting future changes in the index over time. Therefore, the aim of this study is to analyze the trend of VCI changes in Basra province, Iraq, for the period from 2005 to 2022, and to compare three fundamental methods for prediction, including ARIMA-based time series analysis, cubic splines, and the Random Forest algorithm in machine learning. The data related to the studied index were extracted from the Google Earth Engine, and data analysis was conducted using R, SPSS, and ArcGIS software environments. The findings of the study indicated that the general trend of the VCI in Basra province is decreasing, and this index has significantly declined across the province, especially in the southeastern regions. Additionally, the results showed that the predicted data significantly differ from the measured data from the MODIS sensor for the year 2023. The overall conclusion of the study is the relative efficiency of prediction methods for indices such as VCI.
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