Drought Prediction with Deep Learning

In a paper published in the journal MethodsX, researchers reviewed deep-learning (DL) techniques for drought prediction and found that the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are the most used climate indexes.

Study: Drought Prediction with Deep Learning. Image Credit: Piyaset/Shutterstock
Study: Drought Prediction with Deep Learning. Image Credit: Piyaset/Shutterstock

In contrast, the normalized difference vegetation index (NDVI) is the most common multispectral indicator. The study also noted that Asia and Oceania produce the most research in this field, whereas America and Africa have fewer publications.

The long short-term memory network (LSTM) is the most popular DL method, often used alone or with other techniques. The review highlights a need for more research on drought prediction using multispectral indices and DL in America and Africa, suggesting an opportunity for further study in developing countries.

Related Work

Past research on drought prediction using artificial intelligence (AI) techniques has been predominantly concentrated in Asia and Oceania, particularly in Iran, India, and Australia, which are frequently affected by drought due to their arid climates. In contrast, Africa, Europe, and America have fewer publications, with the United States leading in the Americas. Simulations using global circulation models (GCMs) indicate that drought conditions will intensify in regions like the United States, Mexico, Latin America, Europe, the Middle East, North and South Africa, Australia, and parts of China.

Despite this, many drought prediction studies are produced in regions that do not directly apply the analysis, underscoring the need for better local collaboration. Key indices used in these studies include the SPI and the SPEI, with SPEI widely used in arid regions and the NDVI being the most common multispectral index globally.

Drought Prediction with DL

This section explores DL implementations for drought prediction, focusing on studies that use multivariate remote sensing data in various case studies. The analysis aims to identify the DL algorithms employed for drought prediction, using criteria such as drought prediction or forecasting, DL, neural networks (NN), and remote sensing or satellite images.

Various DL algorithms, including conventional models and hybrid approaches, are reviewed. Artificial NN (ANNs) have become popular for drought prediction due to their ability to handle the nonlinearity inherent in drought data. For instance, one study used a deep feedforward NN (DFNN) to monitor agricultural drought in South Asia, outperforming machine learning models like the distributed random forest (DRF) and gradient booster machine (GBM).

Another study combined generalized additive models (GAM) and ANNs to predict drought conditions up to three months in advance, effectively reducing the model space and training time. Similarly, another research demonstrated that using satellite images and vegetative indices, a customized convolutional NN (CNN) model outperformed AlexNet and visual geometry group network (VGGNet) for agricultural drought prediction.

Recurrent NN (RNNs), particularly LSTM networks, are well-suited for time series analysis, making them effective for drought prediction. One study utilized LSTM networks to predict soil moisture, showing superior performance to the autoregressive integrated moving average (ARIMA) model. Another approach used standard and entity-aware LSTMs (EA-LSTMs) to predict vegetation condition index (VCI), with both models outperforming multi-layer perceptron neural networks (MLP) in terms of root mean square error (RMSE).

Further research proposed a short-term drought prediction model combining Convolutional LSTM (ConvLSTM) and RF approaches. This model captures temporal patterns from historical drought conditions and incorporates static and predicted climate variables. ConvLSTM's integration of convolutional operations within LSTM equations allows for effective time series prediction while considering spatial characteristics, making it advantageous over traditional LSTM methods.

Advanced Drought Prediction

Recent advancements in drought prediction utilize generative adversarial networks (GANs), which integrate CNN and LSTM technologies to forecast spatio-temporal drought variations in Africa. This innovative model, structured with a generator (G) and discriminator (D), utilizes multivariate remote sensing data from 1999 to 2022, focusing on agricultural drought assessment through parameters like the SMI. By optimizing the minimax game framework, the GAN accurately simulates and matches time series distributions, while the discriminator enhances prediction fidelity by distinguishing between generated and real data.

Another notable approach employs a CNN-LSTM hybrid model to analyze drought dynamics in South Korea from 2003 to 2019. Integrating precipitation, temperature, NDVI, and soil moisture content as predictor variables, this model predicts groundwater storage changes (GWSC) using Bayesian-optimized hyperparameters.

By employing copula functions and advanced statistical methods, the study elucidates complex relationships between meteorological, agricultural, and groundwater droughts, offering insights crucial for effective drought management strategies tailored to diverse climatic scenarios. These methodologies highlight the evolution towards more sophisticated deep learning frameworks for precise and actionable drought prediction worldwide.


In summary, drought prediction was deemed crucial due to its profound human impacts, prompting the scientific community to utilize deep learning techniques for improved accuracy. This review specifically focused on global applications of DL in drought prediction, emphasizing the prevalence of LSTM networks and climatic indices like SPI and SPEI. Regions such as Asia and Oceania showed significant research interest, while America and Africa had comparatively fewer studies.

Remote sensing indices such as NDVI from moderate resolution imaging spectroradiometer (MODIS) and Landsat played a pivotal role, providing consistent data for vegetation analysis. Despite its computational demands, LSTM, often in hybrid models, remained widely utilized, with RMSE as the primary performance metric. The review underscored the need for expanded deep learning applications, particularly in Latin America, where drought monitoring networks were insufficient.

Journal reference:
Silpaja Chandrasekar

Written by

Silpaja Chandrasekar

Dr. Silpaja Chandrasekar has a Ph.D. in Computer Science from Anna University, Chennai. Her research expertise lies in analyzing traffic parameters under challenging environmental conditions. Additionally, she has gained valuable exposure to diverse research areas, such as detection, tracking, classification, medical image analysis, cancer cell detection, chemistry, and Hamiltonian walks.


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