Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha

Zhang, Xiaobo; Mohanty, Sachi Nandan; Parida, A.K.; Pani, Subhendu Kumar; Dong, Bin; Cheng, Xiaochun

doi:10.1109/access.2020.2972435

Cited by 54 publications

(10 citation statements)

References 16 publications

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“…India being an agricultural country depends on rainfall for source of water. The rainfall analysis is done for region of Karnataka [2]. The climatic feature rainfall affects the agriculture in turn affecting yield of crop and economy of state.…”

Section: Imentioning

confidence: 99%

Untitled

2020

IJCT

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Meteorological data analysis is obtaining the information from raw data. There is vast amount of data available for weather analysis. Market needs timely and accurate data. The collection and datawarehouse of weather data is important because it provides an economic benefit but the local or national economic needs are not as dependent on high data quality as is the weather risk market. The semi arid region of Karnataka namely Madikeri region is considered for data analysis. The relative importance of features is identified for analysis of rainfall data. The adaptive boosting random forest classifier is applied to generate decision rules governing the prediction of rainfall. The data is collected from Indian Meteorological Department (IMD) for span of 12 years from 2004 to 2016. There are 4825 samples considered for the data analysis. The number of features considered for data analysis is 13 for prediction of rainfall. The validation curve and RMSE values justify the results obtained.

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Section: Imentioning

confidence: 99%

Untitled

2020

IJCT

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“…Many researchers have proposed different ways to predict rainfall in Rajasthan (Dutta et al, 2013;Singh et al, 2012b), in India (Dubey, 2015a;Zhang et al, 2020b) as well as in abroad (Zaman, 2018;Kashiwao et al, 2017). There are methods that use multiple parameters for rainfall forecasting Zhang et al (2020a); Pham et al (2020), while some time-seriesbased methods use a single parameter (Sahai et al, 2000;Singh, 2017).…”

Section: Related Workmentioning

confidence: 99%

Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network

Bajpai¹,

Bansal²,

Verma³

et al. 2020

Preprint

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Rainfall is a natural process which is of utmost importance in various areas including water cycle, ground water recharging, disaster management and economic cycle. Accurate prediction of rainfall intensity is a challenging task and its exact prediction helps in every aspect. In this paper, we propose a deep and wide rainfall prediction model (DWRPM) and evaluate its effectiveness to predict rainfall in Indian state of Rajasthan using historical time-series data. For wide network, instead of using rainfall intensity values directly, we are using features obtained after applying a convolutional layer. For deep part, a multi-layer perceptron (MLP) is used. Information of geographical parameters (latitude and longitude) are included in a unique way. It gives the model a generalization ability, which helps a single model to make rainfall predictions in different geographical conditions. We compare our results with various deep-learning approaches like MLP, LSTM and CNN, which are observed to work well in sequence-based predictions. Experimental analysis and comparison shows the applicability of our proposed method for rainfall prediction in Rajasthan.

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“…Among the scienti c study works on weather forecasting, there are a few that should be highlighted. (Zhang et al 2016) (Zhang et al 2020) was the rst to implement ANN, a key Soft Computing approach for weather forecasting. In the prediction of atmospheric data, fuzzy logic is found to be quite useful.…”

Section: Introductionmentioning

confidence: 99%

Monthly Average Rainfall Forecasting Based On An Adaptive Neuro-Fuzzy Inference System in Upper Brahmani Basin, Odisha, India

Rao

Patra

Sasmal

2022

Preprint

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Floods disrupt human activities, resulting in the loss of lives and property of a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of its complexity, understanding and forecasting rainfall is incredibly a challenge. This study investigates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting rainfall using several surface weather parameters as predictors. An ANFIS model is developed for forecasting rainfall over the Upper Brahmani Basin by using 30 years of climate data. A hybrid model with six membership functions gives the best forecast for an area. The suggested method blends neural network learning capabilities with language representations of fuzzy systems that are transparent. The application of ANFIS is to the upper Brahmani river basin is tried for the first time. The ANFIS model with various input structures and membership functions has been built, trained, and tested to evaluate the capability of the model. Statistical performance indices are used to evaluate the performance. Using the developed model, forecast is done for year 2021 – 2030.

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Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha

Cited by 54 publications

References 16 publications

Untitled

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Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network

Monthly Average Rainfall Forecasting Based On An Adaptive Neuro-Fuzzy Inference System in Upper Brahmani Basin, Odisha, India

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