Drought prediction over the East Asian monsoon region using the adaptive neuro‐fuzzy inference system and the global sea surface temperature anomalies

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

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“…Layer 5: this layer contains the output nodes which compute the total average of output through a sum of entire input signals [27]:…”

Section: Anfis: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

et al. 2019

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“…For a statistical indicator to effectively measure drought in terms of meteorological, agricultural, and hydrological aspects, many different types of drought indices have been developed and applied worldwide (Jung and Chang, ; Leng et al ., ; Awan and Bae, ; Lee et al ., ). In particular, the standardized drought indices considering different durations of drought (e.g., 1, 3, 6, 9, and 12 months) have been widely used to identify and assess the drought impacts on climate change because of their convenience, simplicity, and flexibility (Duan and Mei, ; Marcos‐Garcia et al ., ).…”

Section: Methodsmentioning

confidence: 99%

Added value of dynamical downscaling for hydrological projections in the Chungju Basin, Korea

Lee

et al. 2018

Intl Journal of Climatology

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In this study, we assess whether dynamical downscaling using a regional climate model (RCM) would provide added value against the driving global climate model (GCM) and whether a statistically bias-corrected GCM could be used as an alternative to a bias-corrected RCM for assessing the hydrological response to climate change. The target region is the Chungju Basin in Korea, which is characterized by complex geographical features. The analysis focuses on runoff simulations driven by raw outputs and bias-corrected outputs from the HadGEM2-AO and RegCM4, which are used to generate national standard climate change scenarios throughout the Korean Peninsula. Statistical methods, such as linear scaling and quantile mapping, improve the performance in precipitation simulations and effectively correct the systematic biases seen with the climatological means of the HadGEM2-AO and RegCM4. Although there is no relevant difference between the bias-corrected HadGEM2-AO and RegCM4 in terms of long-term averaged climatology, the biascorrected RegCM4 performs better in capturing the dominant mode of runoff variability and hydrological extremes (e.g., floods measured by 1-day maximum runoff and droughts measured by the standardized runoff index). Because the magnitude of the bias correction factor tends to influence the preservation of future change signals derived from raw and bias-corrected outputs, the HadGEM2-AO, which requires a large bias correction factor to compensate for poor performance, produces future changes in high flows with low coherence between the raw and biascorrected projections.

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“…There are different types of SC models, other than ANN-based models, such as ANFIS and SVM, for estimating SST or other parameters (Awan and Bae 2016;Sharafati et al 2020). Fuzzy logic-based models originated from Zadeh (1965), who introduced the fuzzy logic (FL) rules to describe non-linear relations between inputs and outputs.…”

Section: Ann-based Models For Sst Prediction Compared To Other Modelsmentioning

confidence: 99%

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

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The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

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Drought prediction over the East Asian monsoon region using the adaptive neuro‐fuzzy inference system and the global sea surface temperature anomalies

Cited by 17 publications

References 40 publications

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Added value of dynamical downscaling for hydrological projections in the Chungju Basin, Korea

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

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