Adaptive EEMD-ANN hybrid model for Indian summer monsoon rainfall forecasting

Johny, Kavya; Pai, Maya L.; Adarsh, S.

doi:10.1007/s00704-020-03177-5

Cited by 27 publications

(12 citation statements)

References 37 publications

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“…Previous studies have found that hybrid EEMD-ANN and EEMD-SVM models outperformed the classical models, which apply original datasets in other fields of studies, e.g., runoff (Tan et al, 2018); streamflow forecasting (Zhang et al 2015); rainfall forecasting (Johny et al 2020) wind speed forecasting (Yu, 2020); groundwater level (Gong et al, 2018). The hybrid model is robust, theoretically justified, and more realistic compared to other standalone models.…”

Section: Discussionmentioning

confidence: 91%

“…However, this outcome is not surprising, and it can be underlined by analyzing the TSF pattern of three categories. This work proposed a prediction strategy for TSF prediction circumventing the probable precision reduction triggering from calibrating the decomposition method during implementing and accepting the application of operational research reported in several previous works (Napolitano et al, 2011;Zhang et al, 2015;Johny et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…2020;Kamangir et al, 2020). The hybrid EEMD integrated machine learning models have successfully applied in different fields of studies, e.g., runoff (Tan et al, 2018); streamflow forecasting (Zhang et al 2015); rainfall forecasting (Johny et al 2020) wind speed forecasting (Yu, 2020); groundwater level (Gong et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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Accurate thunderstorm frequency (TSF) prediction is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed a novel hybrid machine learning model through hybridization of data pre-processing Ensemble Empirical Mode Decomposition (EEMD) with two state-of-arts models namely artificial neural network (EEMD-ANN), support vector machine (EEMD-SVM) for TSF prediction at three categories of yearly frequencies over Bangladesh. We were demarcated the yearly TSF datasets into three categories for the period 1981-2016 recorded at 28 sites; high (March-June), moderate (July-October), and low (November-February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared 2 with classical ANN, SVM, Autoregressive Integrated Moving Average (ARIMA). EEMD-ANN and EEMD-SVM hybrid models showed 8.02%-22.48% higher performance precision in terms of root mean square error (RMSE) compared to other models at high, moderate and low-frequency categories. Eleven out of 21 input parameters were selected based on the Random Forest (RF) variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid model developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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“…It is designed using SVR-FFA and it is trained and tested using monthly rainfall data. Johny et al [28] designed a framework named AEEMD-ANN for rainfall forecasting and the experimental outcomes show that it is flourishing in predicting SWM precipitation of year 2002. Samantaray et al [29] used RNN, ANFIS and SVM combinations for precipitation investigation.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Rainfall Forecasting Model Using Machine Learning With Singular Spectrum Analysis

Reddy

Sucharitha²,

Narayana³

2022

IIUMEJ

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Agriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall. ABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. Hasil eksperimen menggambarkan bahawa model yang dicadangkan dapat meramalkan hujan secara produktif.

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“…[61] studied the climatic variables responsible for ISMR and used deep learning feature for monsoon rainfall prediction.This study also shows the monsoon deviation from long period average (LPA) rainfall. Johny et al used an adaptive Ensemble Model of ANN which was capable of capturing very low and very high rainfall in the Indian state of Kerala [32]. Dubey et.…”

Section: Related Workmentioning

confidence: 99%

A Deep and Wide Neural Network-based Model for Rajasthan Summer Monsoon Rainfall (RSMR) Prediction

Bajpai¹,

Bansal²

2021

Preprint

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Importance of monsoon rainfall cannot be ignored as it affects round the year activities ranging from agriculture to industrial. Accurate rainfall estimation and prediction is very helpful in decision making in the sectors of water resource management and agriculture. Due to dynamic nature of monsoon rainfall, it's accurate prediction becomes very challenging task. In this paper, we analyze and evaluate various deep learning approaches such as one dimensional Convolutional Neutral Network, Multi-layer Perceptron and Wide Deep Neural Networks for the prediction of summer monsoon rainfall in Indian state of Rajasthan.For our analysis purpose we have used two different types of datasets for our experiments. From IMD grided dataset, rainfall data of 484 coordinates are selected which lies within the geographical boundaries of Rajasthan. We have also collected rainfall data of 158 rain gauge station from water resources department. The comparison of various algorithms on both these data sets is presented in this paper and it is found that Deep Wide Neural Network based model outperforms the other two approaches.

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Adaptive EEMD-ANN hybrid model for Indian summer monsoon rainfall forecasting

Cited by 27 publications

References 37 publications

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Development of Rainfall Forecasting Model Using Machine Learning With Singular Spectrum Analysis

A Deep and Wide Neural Network-based Model for Rajasthan Summer Monsoon Rainfall (RSMR) Prediction

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