Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada (original) (raw)
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Atmospheric Research, 2015
The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.
Monthly and seasonal hydrological drought forecasting using multiple extreme learning machine models
Engineering Applications of Computational Fluid Mechanics , 2022
Hydrological drought forecasting is a key component in water resources modeling as it relates directly to water availability. It is crucial in managing and operating dams, which are constructed in rivers. In this study, multiple extreme learning machines (ELMs) are utilized to forecast hydrological drought. For this purpose, the standardized hydrological drought index (SHDI) and standardized precipitation index (SPI) are computed for 1 and 3 aggregated months. Two scenarios are considered, namely, using SHDI in previous months as the input, and using SHDI and SPI in previous months as the input. Considering these scenarios and two timescales (1 and 3 months), 12 input-output combinations are generated. Then, five different ELMs and support vector machine models are used to predict the SHDI on both timescales. For preprocessing of the data, the wavelet is hybridized with the models, leading to 144 different models. The results indicate that ELMs are capable of forecasting SHDI with high precision. The self-adaptive differential evolution ELM outperforms the other models and the wavelet has a highly positive effect on the model performance, especially in error reduction. In general, using ELMs in hydrological drought forecasting is promising and this model can feasibly be used for this purpose.
Focal Area(s) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics-or knowledge-guided AI Science Challenge Impacts of climate are usually felt through extreme events such as droughts, floods, thunderstorms, windstorms, wildfires, and so on, that are intimately tied to the water cycle. Predicting the frequency and severity of extreme events under climate change remains a significant challenge; meanwhile, the mechanisms and impacts of these extremes are far from well understood. There are several major science challenges: (1) Lack of labelled extreme events data and missing standards in defining extremes; (2) Computational demand of high-resolution ensemble climate modeling; (3) Modeling the multiscale multi-physics hierarchical structure of compound extremes; (4) Lack of understanding of mechanisms of extreme events; (5) Large uncertainty in extreme events impacts on infrastructure; (6) Subjective assessment of weatherrelated risk from seasonal to multi-decadal time scales and lack of metrics for risk assessment and mitigation control. We identify the following high-priority research needs that artificial intelligence (AI), machine learning (ML) and deep learning (DL) may enable transformational breakthroughs, by integrating general purpose GPU, cloud, and edge computing as well as database management: