The document describes different approaches for predicting the percentage of agricultural land irrigated in Indian villages. It tests random forest, clustering, bagging, boosting, linear regression, lasso, ridge and principal component analysis models. Bagging outperforms other models with the lowest RMSE and highest lift. The most important predictive features are found to be power supply, electricity access and education levels. The model could help governments forecast irrigation needs and support farmers' planning.

1. OPTIMIZING
IRRIGATION
TEAM-O
SONALI GULERIA
INCHARA B. DIWAKA

2. CONTENT
▸OBJECTIVE
‣ DATASET
‣ DATA PRE-PROCESSING
‣ BASELINE MODEL
‣ PREDICTION APPROACH 1
‣ PREDICTION APPROACH 2
‣ PREDICTION APPROACH 3
‣ MODEL COMPARISON : CHOOSING THE MODEL
‣ RECOMMENDATIONS

3. OBJECTIVE
‣ Build a prediction engine which can be used
by the government to forecast/budget the
irrigation demands of a village as well as
allow the farmers to plan their irrigation
methods.
‣ The model is used to predict Percentage of
Agricultural Land Irrigated.
‣ This will help to reduce the dependence on
rainfall and strengthen farming decisions by
following more empirical approach.

4. DATASET
▸Source: India Open data portal
▸Picked the states with a diverse socio-economic conditions
to have a broad representation of the data.
▸350+ dimensions spread across various domains-
population, education, irrigation sources, household etc at a
village level.
▸Merged rainfall data to add more information to the data.

5. DATA PRE-PROCESSING
SANITAT
IONAGRO
EDUCATI
ON
LAND
USAGE
CONNECTI
VITY
WATER
INDEXED
DATA

6. FEATURE SELECTION
DATA PRE-PROCESSING
▸Exhaustive Backward feature
selection.
▸Reduced the features from 51
(Indexed data) to 17 most
reflective features.
FEATURE
District Name
Total Geographical Area in Hectares
Total Population of Village
EDUCATION_GOVT
Water
Sanitation
AGRO_Rating
Power Supply For Agriculture Use Status (Active: 1 NA 2)
Power Supply For Commercial Use Summer April Sept per day in Hours
Power Supply For All Users Status (Active: 1 NA 2)
Agro_commodity
Manufacture
Area under Non Agricultural Uses in Hectares
Cultivable Waste Land Area in Hectares
Fallows Land other than Current Fallows Area in Hectares
Current Fallows Area in Hectares
Net Area Sown in Hectares

7. BASELINE MODEL
‣ Random Forest on all 350+ raw
predictors.
‣ Root Mean Square Error: 12.2
CLUSTER
ING
‣ Default Model- Mean of Percentage
Irrigated
‣ Root Mean Square Error: 29.16
REGRES
SION

8. PREDICTION PART 1
▸ State-wise clustering
using Partitioning Around
Mediods (PAM)
▸ 2 clusters based on
highest silhouette width.
CLUSTER
ING
THE BIG
PICTURE

9. ▸10-cross validation- tuning on MTRY parameter.
▸Achieved R-square of approx. .95 for 12 mtry
PREDICTION PART 1(CONTD)
BAGGING
BOOSTIN
G
▸Gradient Boosting algorithm trained on parameters-
n.trees and interaction depth with constant learning
rate and minimum number of observations in trees.
LINEAR
REGRESSION
▸10-cross validation performed to calculate OLS estimates

10. ▸Performed 10- cross
validation to perform
regularized regression on
both raw and indexed Data.
▸Better performance for
Indexed Data.
▸Lasso (penalty, alpha=1)
outperformed ridge (alpha
=0) and average (alpha
=0.5).
PREDICTION PART 2
REGULARIZED
REGRESSION
Top Left: Lasso (MSE vs log(Lamda)) Top Right: Mid (MSE vs log(Lamda))
Bottom: Ridge (MSE vs log(Lamda)) Bottom Right: RMSE comparison of all three

11. ▸Performed PCA on both raw and indexed data.
▸Higher performance by indexed data.
▸Performed 10-cross validation regression using various top
principal components.
PREDICTION PART 3
PRINCIPAL
COMPONENT
ANALYSIS
Principal Component Variance R-squared CV RMSE
PC5 (Knee) 52.00% 0.28 24
PC15 81.22% 0.436 21.4
PC19 89.78% 0.498 20.1

12. MODEL COMPARISON
▸Bagging outperforms with the lowest RMSE and Highest Lift.
DEFAULT MODEL RMSE MODEL RMSE LIFT
Random Forest 12.2 Bagging 5.12 0.580328
Random Forest 12.2 Boosting 7.62 0.37541
Mean 29.16
Linear
Regression 13.8 0.526749
Mean 29.16 Lasso 18.2 0.375857
Mean 29.16 Ridge 18.3 0.372428
Mean 29.16 Mid 18.3 0.372428
Mean 29.16 PC model 23.9 0.180384

13. RECOMMENDATIONS
▸The most important features in our data set are- Power
supply, Electricity and Education.
▸There are various other features that have more of a
correlated relationship and present an empirical
representation of socio-economic condition of villages.
▸These features can be used by government to drive the
budget and other village related policies.
▸Model can be further advanced by incorporating other rich
features like temperature, soil composition and type.