Adaptive Neuro–Fuzzy Inference System for Drought Forecasting in the Cai River Basin in Vietnam

Nguyen, Luong Bang; Li, Qiong Fang; Ngoc, Trieu Anh; Hiramatsu, Kazuaki

doi:10.5109/1543403

Cited by 22 publications

(6 citation statements)

References 38 publications

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“…Shirmohammadi et al 2013 compared artificial neural networks, ANFIS, and support vector machines. Nguyen et al (2015) used SPI values for monitoring and forecasted using Fuzzy logic and ANFIS. and reported that the latter shows the best model even for the long and short-term time scales.…”

Section: Many Researchers Have Compared Standardizedmentioning

confidence: 99%

Forecast of Drought Using Statistical Approach for Erode District

Induja¹,

M²,

Radha³

et al. 2022

MAJ

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Drought plays a crucial role in agriculture, especially in farming and has a significant impact on the environment. The present study focuses on the forecast of drought using one of the hybrid artificial neural network namely the Adaptive NeuroFuzzy Inference System (ANFIS). For this study, 39 years of monthly precipitation value of Erode district are used. Firstly, using monthly precipitation values, Standard Precipitation Index (SPI) values are computed at three monthly scale since Erode district mainly depends on North-East Monsoon. Secondly, with computed SPI value and mean precipitation value of North-East Monsoon season, different ANFIS forecasting models are constructed with its precursory time period. Further, the outcome of the anticipated ANFIS model and observed values were collated using Root Mean Square Error and Mean Absolute Error values. The model with minimal RMSE value was termed as the best fit model.

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Section: Many Researchers Have Compared Standardizedmentioning

confidence: 99%

Forecast of Drought Using Statistical Approach for Erode District

Induja¹,

M²,

Radha³

et al. 2022

MAJ

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show abstract

“…Shirmohammadi et al (2013) examined support vector machines, artificial neural networks, and ANFIS. Nguyen et al (2015) showed that ANFIS exhibits the best model even for the long and short term time scale and that SPI values were utilised for monitoring and forecasting. The primary goal of this study is to evaluate the drought in Tamilnadu's Coimbatore area using the SPI value.…”

Section: Introductionmentioning

confidence: 99%

Anfis-Enhanced Drought Forecasting in Coimbatore District : A Statistical Approach

Radha,

Shankar,

Induja

et al. 2024

Preprint

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Drought has a significant influence on both in the environment and in the area of agriculture, particularly farming. In this scenario, the Adaptive Neuro-Fuzzy Inference System (ANFIS), one of the hybrid artificial neural networks, is primarily used in this study to anticipate drought. The Coimbatore district's monthly precipitation values for the previous 39 years are used in this study. First, as the Coimbatore district primarily depends on the North-East Monsoon, SPI values are estimated at a 3-month scale using monthly precipitation values. Second, several ANFIS forecasting models are built employing the North-East Monsoon season's mean precipitation value and computed SPI value as inputs. Additionally, RMSE, MAE and coefficient of determination value (R2) were used to combine the results of the projected ANFIS model with the observed values. The best-fitting model was defined as having low RMSE, low MAE, and high R2.

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“…In this regard, SPI is a non-linear method based on the drought prediction by conventional statistical methods with great uncertainty. Recently, the implementation of machine learning (ML) algorithms in SPI estimation and modeling such as support vector regression (SVR) (Belayneh et al, 2014;Borji et al, 2016), extreme learning machine (ELM) (M. Liu et al, 2021;Park et al, 2016), linear genetic programming (LGP) (Mehr et al, 2014), adaptive regression spline (MARS) (Deo et al, 2017), extremely randomized tree (ERT) , adaptive neuro-fuzzy inference system (ANFIS) (Gocić et al, 2015;Nguyen et al, 2015), artificial neural network (ANN) (Banadkooki et al, 2021;Deo and Şahin, 2015), M5 Tree (M5T) (Yaseen et al, 2021), and random forest (RF) (Danandeh Mehr et al, 2020; have presented huge potentials with promising results in estimating and modeling drought compared to conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

Pande

Kushwaha

Orimoloye

et al. 2023

Water Resour Manage

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Precise assessment, monitoring and forecasting of drought phenomena are crucial and play a vital role in agriculture and water resources management in the semi-arid region. In this study, Standardized Precipitation Index (SPI) was used to predict the drought in the upper Godavari River basin, India. Ten combinations were used to predict three SPI timescales (i.e., . The historical data of SPI from 2000 to 2019 was divided into training (75 % of the data) and testing (25 % of the data) models for SPI prediction. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to estimate the SPI. The results were compared and analyzed using statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The main results showed that the SMO-SVM poly kernel model precisely predict the SPI-3 (R 2 = 0.819) and SPI-12 (R 2 = 0.968) values at Paithan station; the SPI-3 (R 2 = 0.736) and SPI-6 (R 2 = 0.841) values at Silload station, respectively.The SMO-SVM PUK kernel showed superiority in the prediction of SPI-6 (R 2 = 0.846) at Paithan station and SPI-12 (R 2 = 0.975) at the Silload station. The competition between SVM-SMO poly kernel and SVM-SMO PUK kernel was observed in the prediction of long setting time (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is superior in the estimation of SPI-3 at both stations. The results of the study showed the efficacy of the SVM-SMO algorithm with various kernel functions in the estimation of multiscale SPI and can be helpful in decision making for water resource management and tackle droughts in the semi-arid region of central India.

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Adaptive Neuro–Fuzzy Inference System for Drought Forecasting in the Cai River Basin in Vietnam

Cited by 22 publications

References 38 publications

Forecast of Drought Using Statistical Approach for Erode District

Forecast of Drought Using Statistical Approach for Erode District

Anfis-Enhanced Drought Forecasting in Coimbatore District : A Statistical Approach

Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

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