Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India

2013 ◽  
Vol 5 (1) ◽  
pp. 73 ◽  
Author(s):  
Meher Janhabi ◽  
Jha Ramakar
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Mahanadi River ◽  
Series Analysis

Monitoring and modelling land surface dynamics in Bermejo River Basin, Argentina: time series analysis of MODIS NDVI data

2013 ◽  
Vol 34 (15) ◽  
pp. 5429-5451 ◽  
Author(s):  
V. Barraza ◽  
F. Grings ◽  
M. Salvia ◽  
P. Perna ◽  
A.E. Carbajo ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Land Surface ◽  
Surface Dynamics ◽  
Modis Ndvi ◽  
Series Analysis

scholarly journals Time Series Analysis and Forecasting of Rainfall for Agricultural Crops in India: An Application of Artificial Neural Network

2020 ◽  
Vol 12 (4) ◽  
pp. 1
Author(s):  
Debasis Mithiya ◽  
Kumarjit Mandal ◽  
Simanti Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Analysis ◽  
Linear Model ◽  
Agricultural Production ◽  
Linear Models ◽  
Monthly Rainfall ◽  
Series Analysis ◽  
Non Linear

Indian agriculture depends heavily on rainfall. It not only influences agricultural production but also affects the prices of all agricultural commodities. Rainfall is an exogenous variable which is beyond farmers’ control. The outcome of rainfall fluctuation is quite natural. It has been observed that fluctuation in rainfall brings about fluctuation in output leading to price changes. Considering the importance of rainfall in determining agricultural production and prices, the study has attempted to forecast monthly rainfall in India with the help of time series analysis using monthly rainfall data. Both linear and non-linear models have been used. The value of diagnostic checking parameters (MAE, MSE, RMSE) is lower in a non-linear model compared to a linear one. The non-linear model - Artificial Neural Network (ANN) has been chosen instead of linear models, namely, simple seasonal exponential smoothing and Seasonal Auto-Regressive Integrated Moving Average to forecast rainfall. This will help to identify the proper cropping pattern.

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