Department of Soil Science, Kerala Forest Research Institute, Peechi, Thrissur, India.
AgFE Department, Indian Institute of Technology Kharagpur, Kharagpur, India.
Environ Monit Assess. 2023 Aug 24;195(9):1090. doi: 10.1007/s10661-023-11631-w.
The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.
由于土地和水资源管理的不可持续以及工业化、城市化和气候变化的加剧，各种水文气象灾害（如极端干旱、严重洪水和飓风）的强度和频率呈上升趋势。本研究专注于使用选定的基于人工神经网络（ANN）的模型预测干旱，以使决策者能够改进区域水资源管理计划和灾害缓解/减少计划。本研究中开发了四个 ANN 模型，即一个常规 ANN 模型和三个混合 ANN 模型：（a）基于小波的 ANN（WANN），（b）基于引导的 ANN（BANN），和（c）基于小波和引导的 ANN（WBANN）。被确定为研究区域最佳干旱指标的标准化降水蒸散指数（SPEI）被用作干旱预测的变量。为了预测研究区域六个气象站的 1 到 3 个月的提前期，分别预测了代表“短期”、“中期”和“长期”干旱条件的三个干旱指标，即 SPEI-3、SPEI-6 和 SPEI-12。本研究考虑了统计和图形指标来评估所开发模型的性能。对于混合小波模型，评估了不同消失矩的 Daubechies 小波和分解水平的性能。表现最佳的引导式基础模型进一步用于分析不同干旱预测的不确定性。在所开发的用于 1 到 3 个月干旱预测的模型中，在 3 个月的提前期内，WANN 和 WBANN 模型的性能优于简单的 ANN 和 BANN 模型，用于 SPEI-3、SPEI-6 和 SPEI-12。WANN 和 WBANN 模型的性能最佳，用于 SPEI-12（MAE = 0.091-0.347，NSE = 0.873-0.982），其次是 SPEI-6（MAE = 0.258-0.593；NSE = 0.487-0.848）和 SPEI-3（MAE = 0.332-0.787，NSE = 0.196-0.825），所有这些模型的提前期均为 3 个月。这一发现得到了 WBANN 分析不确定性的支持，即与 SPEI-6（0.402-1.62）和 SPEI-3（0.474-2.304）相比，SPEI-12 的带宽更窄（0.240-0.898）。因此，WBANN 模型推荐用于干旱事件的早期预警，因为它便于干旱预测结果的不确定性分析。
