Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting

Dehghani, Majid; Saghafian, Bahram; Rivaz, Firoozeh; Khodadadi, Ahmad

doi:10.1007/s12517-017-2990-4

Cited by 23 publications

(12 citation statements)

References 55 publications

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“…As DTs are classified as fast algorithms, they became very popular in ensemble forms to model and predict floods [131]. The classification and regression tree (CART) [132,133], which is a popular type of DT used in ML, was successfully applied to flood modeling; however, its applicability to flood…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

969

469

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

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Section: Decision Tree (Dt)mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

969

469

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“…There exist many methodologies for forecasting drought events based on drought indices, such as regression analysis [9,10], stochastic models [7,[11][12][13][14], probability models [15], artificial intelligence (AI)-based models [16][17][18][19], and dynamic modeling [20,21].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model for Drought Forecasting in the Southern Taiwan Basin

Yeh

Hsu

2019

Water

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The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecasting is an important step toward developing such a system. In this study, we utilized the stochastic, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) in southern Taiwan. We employed data from 1967 to 2006 to train the model and data from 2007 to 2017 for model validation. The results showed that the coefficients of determination (R2) were over 0.80 at each station, and the root-mean-square error and mean absolute error were sufficiently low, indicating that the ARIMA model is effective and adequate for our stations. Finally, we employed the ARIMA model to forecast future drought conditions from 2019 to 2022. The results yielded relatively low SPI values in southern Taiwan in future summers. In summary, we successfully constructed an ARIMA model to forecast drought. The information in this study can act as a reference for water resource management.

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“…In neurons, information is received by dendrites, reaching the body of the cell (including the nucleus and other protective components), stimulating the cell, providing the body with the energy needed for neuron activity, and it operates on the input signals, which is modeled by a simple operation and compared to a threshold level. Then the result is transferred to the next cell by [26], [5], [31]). Mosavi et al [30] reviewed the state of the art applications of ANN in hydrological models along with a comparison with other machine learning models.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In recent years, different types of models including time series [11], [12], neural networks [13]- [18], fuzzy inference systems [19], [20], support vector regression [21]- [23] and copula functions [24]- [26] were utilized for drought forecasting. Most of these researches were carried out for meteorological drought forecasting, and few of them were conducted to forecast the hydrological drought indices [27]- [31]. Due to the complexity and non-linear process of drought, using soft computing methods reaches high attention in drought forecasting.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

et al. 2020

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Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.

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Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting

Cited by 23 publications

References 55 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Stochastic Model for Drought Forecasting in the Southern Taiwan Basin

Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

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