Predictive Analytics for Rainfall Prediction

Predictive Analytics for Rainfall Prediction

Lakshmi S R Asha N

Department of Computer Science and Engineering, Dept. of CSE and Engineering

APS College of Engineering, Kanakpura, Bangalore APS College of Engineering, Kanakpura, Bangalore

K C Gouda,

* CSIR Fourth Paradigm Institute NAL Belur Campus, Bangalore, India

environment where several softwares like MATLAB,

Abstract: Rainfall is important for food production plan, water resource management. India is an agricultural country and its economy is largely based upon productivity. Thus rainfall prediction becomes a significant factor in agricultural countries like India. On the growing importance of Rainfall studies in the climate change scenario and High Performance Computing, different Users starting from a farmer to a scientist to a policy maker needs the rainfall prediction well in advance for their application like crop planning, water storage etc. Data discovery from temporal, spatial and spatio- temporal data is critical for rainfall analysis. However, recent growth in observations and model outputs, combined with the increased availability of geographical data, presents new opportunities for the users to implement new techniques such as predictive analytics for developing a predictor which can be used for multi-scale forecasting of rainfall that is from 24 hour forecast to long-range forecast say 2-3 month in advance forecast. Hence we developed predictive analytics system for the efficient and real time prediction of rainfall over India.

KeywordsAgriculture, Ensemble forecasting, Rainfall Forecasting, Prediction.

1. INTRODUCTION

   Agriculture is the predominant occupation in India, accounting for about 52% of employment. Since, India is an agricultural country and its economy is largely based upon crop productivity. The occurrence of prolonged dry period or heavy rain at the critical stages of the crop growth and development may lead to significant reduce crop yield. Rainfall is important for food production plan, water resource management and all activity plans in the nature. Thus rainfall prediction becomes a significant factor in agricultural countries like India. Rainfall forecasting has been one of the most scientifically and technologically challenging problems around the world in the last century. The rainfall data is available for the data mining techniques which can be useful for predicting the rainfall which can very useful for taking decisions over crop planting in the areas.

   The Irrigation facilities are inadequate, as revealed by the fact that only 52.6% of the land was irrigated in 200910 which result in farmers still being dependent on rainfall, specifically the Monsoon season. A good monsoon results in a robust growth for the economy as a whole, while a poor monsoon leads to a sluggish growth. In the present study a framework for rainfall prediction from past data and present weather condition is generated using the predictive analytics in a High Performance Computing

   STATISTICA etc are interfaced with the object oriented language to develop a multi-scale forecasting platform for rainfall prediction.

2. RELATED WORK

   There have been many attempts to forecast rainfall. Rainfall forecasting can apply to many time horizons such as short term, medium term, and long term periods. Some authors design systems which can forecast yearly data, some try to forecast monthly data whereas some try to forecast daily data.

   N. Sen. [1] has presented long-range summer monsoon rainfall forecast model based on power regression technique with the use of Ei Nino, Eurasian snow cover, north west Europe temperature, Europe pressure gradient Wind pattern, Arabian sea SST, east Asia pressure and south Indian ocean temperature in previous year. The experimental results showed that the model error was 4%.

   1. Nkrintra, [2] described the development of a statistical forecasting method for SMR over Thailand using multiple linear regression and

      local polynomial-based nonparametric approaches. SST, sea level pressure (SLP),wind speed, EiNino Southern Oscillation Index (ENSO), IOD was chosen as predictors. The experiments indicated that the correlation between observed and forecast rainfall.

   2. Sohn, [3] has developed a prediction model for the occurrence of heavy rain in South Korea using multiple linear and logistics regression, decision tree and artificial neural network.

   M. T. Mebrhatu [4] modeled for prediction categories of rainfall (below, above, normal) in the highlands of Eritrea. The most influential predictor of rainfall amount was the southern Indian Ocean SST. Experimental results showed that the hit rate for the model was 70%.

   H. Hasani [5] proposed human height prediction model based on multiple polynomial regression that was used successfully to forecast the growth potentials of height with precision and was helpful in children growth study.

   Vaccari [6] modeled plant motion time series and Nutrient recovery data for advanced life support using multi variable polynomial regression.

   But in these studies the approach is very simple as the data set is very small, To handle data mining in a Big data environment as all the climate data are of large size with multi-dimensional (latitude ,longitude, vertical levels,

   time(day,month,year) ) and of large time scale starting from daily to decades i.e 10 years. So in the present project work algorithms will be developed for large climate data analysis to study the climate change over India.

   Boundary

   Condition

   Boundary

   Condition

   Model

   output+Predic tive Model

   Model

   output+Predic tive Model

   CMMAC

   SGCM

   model

   CMMAC

   SGCM

   model

3. SYSTEM DESIGN

   Initial

   Conditio n

   Initial

   Conditio n

   Analysis

   Algorithm

   Analysis

   Algorithm

   Results

   Results

   Visualization Tools

   Visualization Tools

   Real time

   observation andApplication (nowcasting)

   Real time

   observation andApplication (nowcasting)

   The fig 1 shows the high level design of forecasting a rainfall. In this we consider both boundary conditions (eg

   .It may be land, ocean, forest parameter) as well as initial conditions( ie.,today's condition) data along with the GCM(Global Climate Model) output data. With these output data we apply our predictive analysis algorithm to analyze the data. Finally, the forecasted results will be

   Step 6: Using the trained Predictive model to derive present day rainfall state with the help of Principal components obtained from GCM output and rainfall state of the previous day.

   1. Design of Multi-Scale Predictive Analytics

      • Short Range Prediction(1-6hrs)

        Now casting using real-time satellite data.

        eg. IPL match, event organization, closing school etc.

      • Medium Range Prediction(3-5days)

        Cyclone or heavy rainfall using meso-scale model (WRF)

        +PA algorithm

        Eg. Disaster preparedness, Warning for Fisherman, Closing school.

      • Long Range Prediction(3-6months)

        Monsoon rain forecasting in April for JJAS using GCM Model + PA algorithm

        Eg. Agriculture planning, Water management, food security etc.

      • Climate Prediction(10-100yrs)

   Understanding Climate Change, Global warming using CSIR climate model +PA algorithm

   Eg. Food habit, crop pattern etc

4. IMPLEMENTATION AND RESULTS The modules are

   1. Monsoon model (ong range)

   2. weather research forecast model(Short-range)

   3. Cyclone model (rainfall day cyclone)

   In order to calculate the anomaly of rainfall we use the following formula.

   1 50

   obtained by the visualization package (ie., Grads).

   Fig1. High level design

   Rain(lat,lon) =

   Anomaly

   Rain(lat,lon, yr)

   50 yr = 1

   A. Flow of the Predictive Model

   Step 1: Adopting unsupervised data classification technique, such as K-means clustering technique, for clustering of the observed multi-site rainfall data in order to identify the rainfall states present in the rainfall data.

   Step 2: Perform Principal Component Analysis (PCA) to reduce the dimensions of the standardized predictor data,

   1. CMMACS climate data set. The dimensionally-reduced climate variables represent a large fraction of the variability contained in the original data.

      Step 3: Training the Predictive model(s) to establish relationship between the input data containing current day standardized and dimensionally-reduced climate predictors along with previous day(s) rainfall state and the output data containing the current day rainfall state.

      Step 4: Applying bias correction for the GCM output data to obtain bias-corrected GCM data.

      Step 5: Obtain principal components of GCM data by performing PCA of the bias-corrected GCM data with the help of principal directions obtained during PCA of CMMACS climate data.

      For year if anomaly (-10 to10): Normal Year (ex 1998)

      If anomaly less than (-10): Drought or Deficit Year (ex 2002)

      If anomaly more than (+10): Flood or Excess Year (ex 1961).

      Regression

      Regression is a statistical empirical technique that utilizes the relation between two or more quantitative variables on observational database so that an outcome variable can be predicted from the others.

      Regression use two methods Simple linear regression and multiple linear regression models. Regression produces a polynomial describing the relationship between any set of inputs and corresponding output.

      Here we have considered past data to develop a regression equation,

      • A linear regression equation to predict monsoon rainfall

   Y=aX1+bX2	Year	Rainfall
   Where		Forecast	Observed
   y=Normalized rain anomaly.	1951	748	737
   x1= normalized anomaly of the location. x2=normalized	1952	797	792
   anomaly of January to April.	1953	925	920
   The regression equation is utilized in order to predict	1954	891	885
   the accurate results. The overall measure is adopted for the	1969	872	829
   accuracy of the forecasts is the root-mean-square error	1970	891	939
   (rmse). 1971		911	886
   1972		717	653
   n 1973		921	912
   		767	747
   i1 1975		948	960
   Where fi and oi are the forecast and observed Indian 1976		853	855
   monsoon rainfall for the ith year. In the present work an 1977		805	880
   predictive analytics approach is carried out to provide 1978		845	908
   better understanding of the rainfall pattern over all India 1979		769	746
   using 50 years of multi-source data. This work involves 1980		825	881
   development of several novel algorithms like ensemble 1981		853	842
   forecasting, multi-model forecasting of the monsoon 1982		738	736
   rainfall, extreme rainfall events, flood or drought Index etc. 1983		904	959
   over India using predictive analytics. The developed 1984		845	835
   predictive algorithm is compared against the traditional Table1.Comparison of PA forecasted Rainfall and
   directions. Finally several case studies will be presented Year		Raw	With PA	Observat
   using predictive analytics for rainfall predictions, which		Prediction	Prediction	ion
   provide new scientific insights with high societal impacts. 2012		+3	-2	-7
   Fig.2 shows the results of 20yr of verification of 2011		+6	+4	+2
   observed and forecasted rainfall. The arrow along the 2010		-1	+3	+2
   abscissa denotes the long-term mean rainfall. The two 2009		-5	-8	-22
   dotted lines represent the actual rainfall.	Tab	le2. Rainfall Forecasting f	or all India

   Y=aX1+bX2	Year	Rainfall
   Where		Forecast	Observed
   y=Normalized rain anomaly.	1951	748	737
   x1= normalized anomaly of the location. x2=normalized	1952	797	792
   anomaly of January to April.	1953	925	920
   The regression equation is utilized in order to predict	1954	891	885
   the accurate results. The overall measure is adopted for the	1969	872	829
   accuracy of the forecasts is the root-mean-square error	1970	891	939
   (rmse). 1971		911	886
   1972		717	653
   n 1973		921	912
   		767	747
   i1 1975		948	960
   Where fi and oi are the forecast and observed Indian 1976		853	855
   monsoon rainfall for the ith year. In the present work an 1977		805	880
   predictive analytics approach is carried out to provide 1978		845	908
   better understanding of the rainfall pattern over all India 1979		769	746
   using 50 years of multi-source data. This work involves 1980		825	881
   development of several novel algorithms like ensemble 1981		853	842
   forecasting, multi-model forecasting of the monsoon 1982		738	736
   rainfall, extreme rainfall events, flood or drought Index etc. 1983		904	959
   over India using predictive analytics. The developed 1984		845	835
   predictive algorithm is compared against the traditional Table1.Comparison of PA forecasted Rainfall and
   directions. Finally several case studies will be presented Year		Raw	With PA	Observat
   using predictive analytics for rainfall predictions, which		Prediction	Prediction	ion
   provide new scientific insights with high societal impacts. 2012		+3	-2	-7
   Fig.2 shows the results of 20yr of verification of 2011		+6	+4	+2
   observed and forecasted rainfall. The arrow along the 2010		-1	+3	+2
   abscissa denotes the long-term mean rainfall. The two 2009		-5	-8	-22
   dotted lines represent the actual rainfall.	Tab	le2. Rainfall Forecasting f	or all India

   RMSE (Fi Oi)2 / n

   1974

   statistical methods of forecasting and proposes new

   Observed Rainfall for the period 1951-1984

   140000

   130000

   120000

   110000

   100000

   range

   range

   90000

   80000

   70000

   60000

   l

   l

   50000

   40000

   30000

   20000

   10000

   0

   2003 JJAS rainfall

   June July August Sept

   month

   Fig.2 Observed and forecasted rainfall for 20yr of verification

   Table 1 shows the comparison of forecasted and observed rainfall over India from the period 1951- 1984.where the forecasted results are almost near to the observed data. Table 2 shows the result of forecasted rainfall for monsoon season (June, July, august & September) and this shows that predictive analytics prediction is almost near to observed rainfall data. Fig 3 shows the comparison of predicted model and observed monsoon rainfall.

   Fig 3. Seasonal Rainfall variation in monsoon months

   Rain_Anomaly(1951-2003)

   17.5

   12.5

   Range

   Range

   7.5

   2.5

   -2.5

   -7.5 -20. 8

   -12. 5

   -17.5

   -22.5

   1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2

   9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0

   5 5 5 5 6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0

   Year

   Fig 4. Rainfall anomaly calculated for 53yrs of data

   Fig 3. Shows the seasonal rainfall variation during the months of June-Septembers. The results are averaged over 53 years and fig 4 shows the rainfall anomaly calculated for 53 yrs of data.

5. CONCLUSION

The skill of prediction contributes towards developing methodologies for predicting rainfall at local or regional scale over India from large scale GCM output of climatological data. At the end of present work first time a predictive analytics package is on place using which one can generate accurate and efficient spatio- temporal rainfall forecasting at an affordable cost in a cloud computing and Big Data environment.

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