scholarly journals Seasonal rainfall forecasting in West Africa

Nature ◽  
1974 ◽  
Vol 248 (5448) ◽  
pp. 464-464 ◽  
Author(s):  
Derek Winstanley
Keyword(s):  
West Africa ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

Seasonal rainfall forecasting in West Africa

Nature ◽  
1975 ◽  
Vol 253 (5493) ◽  
pp. 622-623 ◽  
Author(s):  
A. H. BUNTING ◽  
M. D. DENNETT ◽  
J. ELSTON ◽  
J. R. MILFORD
Keyword(s):  
West Africa ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

Can Farmers of Burkina Faso Use Seasonal Rainfall Forecasts?

2000 ◽  
Vol 22 (4) ◽  
pp. 24-28 ◽  
Author(s):  
Carla Roncoli ◽  
Keith Ingram ◽  
Paul Kirshen
Keyword(s):  
West Africa ◽  
Burkina Faso ◽  
Agricultural Production ◽  
Scientific Information ◽  
Seasonal Rainfall ◽  
Seasonal Forecasts ◽  
Recent Event ◽  
Livelihood Security ◽  
Research Findings ◽  
Seasonal Rainfall Forecasts

In this article we bring anthropological reflections to bear on a recent event we participated in, whereby farmers and scientists came together to discuss the possibility of applying rainfall seasonal forecasts to improve agricultural production and livelihood security in West Africa. In so doing, We also report on the research findings from the project that organized this encounter and that we have been working with for the last three years. Our intent is to highlight the complexities and challenges inherent in this process of integrating scientific information and farmers' production decisions, while also pointing to practical issues to be considered in implementing such initiatives.

scholarly journals Use of ENSO-Based Seasonal Rainfall Forecasting for Informed Cropping Decisions by Farmers in the SAT India

2007 ◽  
pp. 165-180 ◽  
Author(s):  
V. Nageswara Rao ◽  
P. Singh ◽  
J. Hansen ◽  
T. Giridhara Krishna ◽  
S. K. Krishna Murthy
Keyword(s):  
Seasonal Rainfall ◽  
Rainfall Forecasting

Long-term seasonal rainfall forecasting: efficiency of linear modelling technique

2018 ◽  
Vol 77 (7) ◽  
Author(s):  
Iqbal Hossain ◽  
H. M. Rasel ◽  
Monzur Alam Imteaz ◽  
Fatemeh Mekanik
Keyword(s):  
Seasonal Rainfall ◽  
Modelling Technique ◽  
Rainfall Forecasting ◽  
Linear Modelling

scholarly journals Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

2016 ◽  
Vol 48 (3) ◽  
pp. 867-882 ◽  
Author(s):  
M. S. Babel ◽  
T. A. J. G. Sirisena ◽  
N. Singhrattna
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Large Scale ◽  
Resource Planning ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Atmospheric Variables

Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall in May–June–July (MJJ), August–September–October (ASO), November–December–January (NDJ), and February–March–April (FMA) and to determine the effects of climate change on seasonal rainfall. LSAVs, temperature, pressure, wind, precipitable water, and relative humidity at different lead times were identified as the significant predictors. To determine the impacts of climate change the predictors obtained from two general circulation models, CSIRO Mk3.6 and MPI-ESM-MR, were used with quantile mapping bias correction. Our results show that the models with the best performance for FMA and MJJ seasons are able to forecast rainfall one month in advance for these seasons and the best models for ASO and NDJ seasons are able do so two months in advance. Under the RCP4.5 scenario, a decreasing trend of MJJ rainfall and an increasing trend of ASO rainfall can be observed from 2011 to 2040. For the dry season, while NDJ rainfall decreases, FMA rainfall increases for the same period of time.

scholarly journals Multivariate Statistical Analysis of the Seasonal Rainfall Regimes of the Guinea-Fouta Djallon Mountains of West Africa.

2002 ◽  
Vol 58 (4) ◽  
pp. 171-183 ◽  
Author(s):  
Serrie KAMARA ◽  
Tilack KURUPPUARACHCHI ◽  
Edmond Ranga RANATUNGE ◽  
Yousay HAYASHI ◽  
Masayuki YOKOZAWA ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
West Africa ◽  
Multivariate Statistical Analysis ◽  
Seasonal Rainfall ◽  
Multivariate Statistical ◽  
Fouta Djallon ◽  
Rainfall Regimes
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