气候变化领域集成服务门户(CCPortal): Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models

CCPortal

DOI	10.1080/10106049.2022.2093411
	Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models
	Wahla, Saadia Sultan; Kazmi, Jamil Hasan; Sharifi, Alireza; Shirazi, Safdar Ali; Tariq, Aqil; Smith, Hayley Joyell
发表日期	2022
ISSN	1010-6049
EISSN	1752-0762
起始页码	14963
结束页码	14982
卷号	37 期号:27
英文摘要	Droughts may inflict significant damage to agricultural and water supplies, resulting in substantial financial losses as well as the death of people and livestock. This study intends to anticipate droughts by studying the changes of an acceptable index using appropriate climatic factors. This study was divided into three phases, first being the determination of the Standardized Precipitation Evapotranspiration (SPEI) index for the Cholistan, Punjab, Pakistan area based on a dataset spanning 1980 to 2020. The indices are calculated at different monthly intervals which could to predict short-term periods for the Cholistan in Pakistan, we selected two distinctive time periods of one month (SPEI-1) and three months (SPEI-3). The second phase involved dividing the data into three sample sizes, which were used for training data from 1980 to 2010, testing data from 2011 to 2015 and validation data from 2016 to 2020. The utilization of the random forest (RF) algorithm to train and evaluate the data using a variety of climate variables e.g. potential evapotranspiration, rainfall, vapor pressure cloud cover, and mean, minimum and maximum, temperature. The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Based on these considerations, statistical measures, such as the Coefficient of Determination (R-2) and the Root Mean Square Error (RMSE) approach, were used to evaluate the performance of the test group throughout the testing period. The model's performance revealed the satisfactory results with R-2 values of 0.80 and 0.78, for SPEI-1 and SPEI-3 situations, respectively. Following the data analysis, it was discovered that the validation period had a receiving operating curve and area under the Curve (ROC-AUC) of 0.87 for the SPEI-1 case and 0.85 for the SPEI-3 case. In this context, the results indicate that the SPEI may be useful as a prediction tool for drought prediction and the performances the RF model was suitable for both timescales. However, a more rigorous analysis with a larger dataset or a combination of datasets from different areas might be more beneficial for generalization over more extended time periods provide additional insights.
英文关键词	Drought forecasting; Standardized Precipitation-Evapotranspiration index; random forest; ROC-AUC
语种	英语
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000819564800001
来源期刊	GEOCARTO INTERNATIONAL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281124
作者单位	University of Karachi; University System of Georgia; University of Georgia; Shahid Rajaee Teacher Training University (SRTTU); University of Punjab; Wuhan University; Mississippi State University; University System of Georgia; University of Georgia
推荐引用方式 GB/T 7714	Wahla, Saadia Sultan,Kazmi, Jamil Hasan,Sharifi, Alireza,et al. Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models[J],2022,37(27).
APA	Wahla, Saadia Sultan,Kazmi, Jamil Hasan,Sharifi, Alireza,Shirazi, Safdar Ali,Tariq, Aqil,&Smith, Hayley Joyell.(2022).Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models.GEOCARTO INTERNATIONAL,37(27).
MLA	Wahla, Saadia Sultan,et al."Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models".GEOCARTO INTERNATIONAL 37.27(2022).