Forecasting drought using neural network approaches with transformed time series data

Evkaya, Ozan; Kurnaz, Fatma Sevinç

doi:10.1080/02664763.2020.1867829

Cited by 12 publications

(6 citation statements)

References 19 publications

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“…Second, the SPI can be computed on any time scale, allowing it to describe different drought types [43,65,66]. Third, since the SPI is a dimensionless index, it is possible to easily compare values across both time and space [26,67].…”

Section: Standardised Precipitation Index (Spi)mentioning

confidence: 99%

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

et al. 2022

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Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively.

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Section: Standardised Precipitation Index (Spi)mentioning

confidence: 99%

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

et al. 2022

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“…ANN is used to model the R-R relationship (Young and Liu, 2015;Vyas et al 2016;Kumar et al 2016;Dounia et al, 2016;Asadi et al 2019), to predict rainfall (Lee et al, 1998;Mirabbasi et al 2019), to predict river flow (Guimaraes Santos and Silva, 2014;Shi et al, 2016;Zemzami and Benaabidate, 2016;Wagena et al 2020;Adnan et al 2021), to predict reference evapotranspiration (Aytek, 2008;Qasem et al 2019;Tikhamarine et al 2019;Elbeltagi et al 2022), to predict discharge and waterlevel (Khan et al, 2016;Nacar et al 2018;Anilan et al 2020;Damla et al 2020;Temiz et al 2021), to predict snowmelt-runoff (Yilmaz, 2011), ANNs have also been regarded as a powerful tool for use in a variety of underground water problems (Malik et al 2021;Wunsch et al 2021). ANNs can be used for other purposes is unit hydrograph derivation (Lange, 1998), flood frequency analysis (Campolo, 2003;Dawson, et al, 2006;, drought analysis (Shin and Salas, 2000;Ochoa-Rivera, 2008;Banadkooki et al 2021;Ozan Evkaya and Sevinç Kurnaz, 2021), suspended sediment data estimation (Jimeno-Sáez, 2018;Khan et al 2019;Meshram et al 2020), Modelling the infiltration process Sihag et al 2021;Singh et al 2021), estimation of hydroelectric generation (Uzlu et al, 2014;Niu et...…”

Section: Literature Surveymentioning

confidence: 99%

Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

Katipoğlu

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.

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“…In the past, multiple regression and autoregressive moving average methods were utilized as conventional models for streamflow forecasting. However, these models rely on static data and work well only when the data has linear distribution and is regularly distributed [6][7][8]. In contrast, streamflow time series data usually has nonlinear and unstable characteristics.…”

Section: Introductionmentioning

confidence: 99%

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Abdul Kareem,

L. Zubaidi,

Al-Ansari

et al. 2024

Computer Modeling in Engineering &Amp; Sciences

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Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance metrics. This study focuses on two types of hybrid models: parameter optimisation-based hybrid models (OBH) and hybridisation of parameter optimisation-based and preprocessing-based hybrid models (HOPH). Overall, this research supports the idea that meta-heuristic approaches precisely improve ML techniques. It's also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches (classified into four primary classes) hybridised with ML techniques. This study revealed that previous research applied swarm, evolutionary, physics, and hybrid metaheuristics with 77%, 61%, 12%, and 12%, respectively. Finally, there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

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Forecasting drought using neural network approaches with transformed time series data

Cited by 12 publications

References 19 publications

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

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