Comparing Machine-Learning Models
for Drought Forecasting in Vietnam’s
Cai River Basin

Liu, Zhen Nan; Li, Qiong Fang; Nguyen, Luong Bang; Xu, Gui Hong

doi:10.15244/pjoes/80866

Cited by 23 publications

(10 citation statements)

References 55 publications

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“…The estimated outcomes of SPI-6 and SPI-3 showed that the WANN model exhibited superior performance to other models. Liu et al [51] used the Self-Adaptive Evolutionary-Extreme Learning Machine (SADE-ELM), Online Sequential-ELM (OS-ELM), and ELM for meteorological drought prediction based on SPEI and SPI in Khanhhoa Province, Vietnam. They found that the performance of the SADE-ELM models was superior to the other models.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Malik

Kumar

Rai

et al. 2021

Climate

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Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

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Section: Discussionmentioning

confidence: 99%

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Malik

Kumar

Rai

et al. 2021

Climate

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“…Moreover, both methods have been proved to be computationally efficient. Various machine learning approaches have been extensively applied in the case of drought prediction (see, for example, [[28], [29], [30], [31], [32]]). The most recent work by Fung et al [33] provides a comprehensive review of the applications of statistics-based modelling as well as machine learning methods for drought forecasting over the period from 2007 to 2017.…”

Section: Methods Detailsmentioning

confidence: 99%

Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods

Kuswanto

Naufal

2019

MethodsX

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“…Artificial neural networks and support vector regression were used by [3] to create a model to understand the effect of drought in the New Wales region of South in Australia; The extreme learning machine (ELM) was used to identify drought situations by predicting SPI and SPEI in the Cai River Basin in Vietnam [19]; to name just those…”

Section: Word Cloud Of Drought Prediction Algorithmsmentioning

confidence: 99%

Drought Forecasting: A Bibliometric Analysis and Future Research Directions

Vodounon¹,

Soude²,

Mamadou³

2022

JEP

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Droughts represent one of the most dangerous natural disasters in the world, due to their ability to progressively spread over large areas up to continental scale, as well as their adverse environmental, human and socio-economic effects. Unfortunately, these effects are increasingly accentuated under the influence of climate change. One of the main challenges today is to mitigate the damage associated with droughts by developing tools capable of predicting the occurrence of such events in advance. Many solutions have been implemented for this purpose. But with the great progress in artificial intelligence, many scientists propose the use of Machine Learning to provide more optimal solutions to the problem related to droughts. In the present study, a bibliometric analysis was conducted to assess the current level of research on forecasting and monitoring of droughts in the world in general, particularly in West Africa through different methods based on the artificial intelligence. A search for articles on the topic was performed in the Web of Science (WoS) database, which is a global, publisher-independent citation database. The search identified a total of 3284 documents and the collected data was analyzed using a bibliometric tool called Bibliometrix. The main results are presented and discussed, followed by some potential avenues for research.

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Comparing Machine-Learning Models for Drought Forecasting in Vietnam’s Cai River Basin

Cited by 23 publications

References 55 publications

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods

Drought Forecasting: A Bibliometric Analysis and Future Research Directions

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