Agricultural drought forecasting using satellite images, climate indices and artificial neural network

Marj, Ahmad Fatehi; Meijerink, A.M.J.

doi:10.1080/01431161.2011.575896

Cited by 73 publications

(28 citation statements)

References 21 publications

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“…Stochastic models are linear models with limited ability to predict nonlinear data. To effectively predict nonlinear data, an increasing number of researchers have begun to use artificial neural networks (ANNs) to predict hydrological data in the past decade (Kousari et al 2017;Seibert et al 2017;Marj and Meijerink 2011;Ochoa-Rivera 2008;Sigaroodi et al 2013). Artificial neural networks have been used as drought prediction tools in many studies (Seibert et al 2017;Borji et al 2016;Deo and Ş ahin 2015;Chen et al 2017;Belayneh and Adamowski 2012;Belayneh et al 2016) and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Zhang

Ding

et al. 2020

Journal of Applied Meteorology and Climatology

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Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

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Section: Introductionmentioning

confidence: 99%

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Zhang

Ding

et al. 2020

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

show abstract

“…Unlike physical and conceptual models, data-driven models are not difficult to implement for the purposes of real-time forecasting. Artificial neural networks (ANNs) have been used in several studies as a drought-forecasting tool [10][11][12][13][14][15][16]. The most popular type of ANN used for the purposes of drought forecasting is the multilayer perceptron (MLP) that is usually optimized with a back propagation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

Belayneh

Adamowski

2012

Applied Computational Intelligence and Soft Computing

128

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Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the models was compared using RMSE, MAE, and R 2 . The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.

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“…Since the proposed approach has some uncertainties associated with estimating drought forecasting, combination with a remote sensing dataset (e.g., NDVI) and multiple climate indices (e.g., North Atlantic Oscillation) would provide a better assessment of drought forecasting in the future [74].…”

Section: Discussionmentioning

confidence: 99%

Improved Drought Prediction Using Near Real-Time Climate Forecasts and Simulated Hydrologic Conditions

Kang

Sridhar

2018

Sustainability

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Short-term drought forecasting is helpful for establishing drought mitigation plans and for managing risks that often ensue in water resource systems. Additionally, hydrologic modeling using high-resolution spatial and temporal data is used to simulate the land surface water and energy fluxes, including runoff, baseflow, and soil moisture, which are useful for drought forecasting. In this study, the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models are used for short-term drought forecasting in the contiguous United States (CONUS), as many areas in this region are frequently affected by varying drought intensities. Weekly-to-seasonal meteorological inputs are provided by the Climate Prediction Center (CPC) for the retrospective period (January 2012 to July 2017) and Climate Forecasting System version 2 (CFS v2) for the forecasting period (August 2017 to April 2018), and these inputs are used to estimate agricultural and groundwater drought conditions. For drought assessment, three drought indices, namely, the Standardized Soil Moisture index (SSI), the Multivariate Standardized Drought Index (MSDI), and the Standardized Baseflow index (SBI), were analyzed. The accuracy of the forecasting results was verified using several a performance measure (Drought area agreement (%); DA). Generally, eight weeks of lead time forecasting showed good drought predictability from both the SWAT and VIC models for the MSDI simulations (62% for SWAT and 64% for VIC for all drought categories). However, the DA values for eight weeks lead time forecasting for SSI were 23% (SWAT) and 10% (VIC) and 7% (SWAT) and 7% (VIC) for the SBI, respectively. In addition, the accuracies of drought predictions remarkably decreased after eight weeks, and the average DA values were 36% for SWAT and 38% for VIC due to an increase in the uncertainties associated with meteorological variables in CFS v2 products. For example, there are increases in the total number of grids where the absolute values of monthly differences between CFSv2 and CPC observations exceed 20 mm and 1 • C during the forecasting period. Additionally, drought forecasting using only one variable (i.e., SSI and SBI) showed low prediction performances even for the first eight weeks. The results of this study provide insights into drought forecasting methods and provide a better understanding to plan for timely water resource management decisions.

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Agricultural drought forecasting using satellite images, climate indices and artificial neural network

Cited by 73 publications

References 21 publications

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

Improved Drought Prediction Using Near Real-Time Climate Forecasts and Simulated Hydrologic Conditions

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