Department of Geography, Institute of Science, Banaras Hindu University, Uttar Pradesh, Varanasi, 221005, India.
Environ Sci Pollut Res Int. 2024 Nov;31(54):63098-63119. doi: 10.1007/s11356-024-35398-w. Epub 2024 Oct 29.
Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.
干旱作为一种自然而复杂的气候现象，对自然生态系统和社会经济条件都带来了挑战。评估干旱特征是一项重要的努力，旨在减轻其对社会和个人的影响。本研究论文探讨了将标准化降水蒸散指数（SPEI）与机器学习技术相结合，评估印度重要农业气候区——恒河中上游平原干旱特征的方法。该研究重点评估了干旱事件的频率、强度、幅度和重现期。研究采用了各种干旱模型，包括随机森林（RF）、人工神经网络（ANN）和结合 ANN 和 RF 的集成模型，分析和预测不同时间尺度（3 个月、6 个月和 12 个月）的干旱模式。通过使用精度、准确性、错误分类比例、整体曲线下面积（AUC）、平均绝对误差（MAE）和均方根误差（RMSE）等关键指标，对这些模型进行了严格验证。此外，研究还通过为每个模型和时间尺度划定高和极高干旱脆弱性区域，将其应用扩展到划分干旱脆弱性区域。结果表明，恒河中上游平原的西南部属于高度干旱脆弱区，平均占研究区域的 40%。还确定了干旱脆弱性的核心和缓冲区。研究区的西南部被确定为干旱的核心区。通过使用土壤湿度计对干旱脆弱区进行了实地验证。验证指标表明，ANN 和 RF 的集成模型在所有时间尺度上都具有最高的准确性。本研究的结果可用于提高恒河中上游平原的干旱准备和水资源管理水平。通过识别高风险干旱区和利用准确的预测模型，政策制定者和农民可以实施有针对性的缓解策略。这种方法可以增强农业的弹性，保护生计，并优化这个重要农业气候区的水资源分配。
