Water is a critical resource for sustainable economic and social development of a country. To maintain health & hygiene, energy & agricultural products, and the environment management water plays a key role. Water demand prediction is essential to analyze the requirement that indicate emergency state for water management decisions.
Using water demand prediction we can determine how much gallon of water is required in the particular area.
This can vary from place to place and it depends on climate also For example in summer huge gallon of water is required compared to winter so this can be determined using this project.
So depending upon the weather condition we can store water from natural resource like rain for this we are using the supervised learning which determines the output from the previous input values, here we consider the data from past 10 years.
Water is important resource for a country's sustainable economic and social growth. Water plays a key role in maintaining health & hygiene, energy & agricultural products and the management of the environment. Prediction of water demand is essential for analyzing the requirement for water management decisions that indicate an emergency state.
Water plays an important part in one's life. Considering water shortage, and Its inherent constraints, the management of demand and forecasts in this field Definitely significant. There were many soft computing techniques built over the Predictions for water demand last few decades. This study targets soft computing Water intake processes. Those approaches include neural artificial networks (ANNs), Fuzzy and neuro-fuzzy models, vector machine support, metaheuristics, and device support.
Hardware requirement:
CPU: 2 x 64-bit, 2.8 GHz with 8.00 GT/s CPUs. RAM: 32 GB or 16 GB of 1600 MHz DDR RAM Storage: 300 Gingernut connectivity and use for downloading files from Anaconda Cloud or program, or using a USB drive that contains all the files users need with alternate instructions for air gapped installations.
Software requirement:
• Anaconda
• Jupiter
• Notebook
The lifecycle includes some of the main stages usually performed by companies, mostly iterative ly:
import pandas as pd
import numpy as np
data=pd.read_csv("data.csv")
#data1=data.copy(deep=False)
data2=data.copy(deep=True)
data2.index
data2.columns
data2.shape
data2.memory_usage()
#using linear regression to predict
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
reg=LogisticRegression()
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=4)
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train,y_train)
ypred=knn.predict(x_test)
print(ypred)
y2= data3[' WATER DELIVERIES TO Single.family.Residential ']
x2=data3[cols1]
x_train2,x_test2,y_train2,y_test2=train_test_split(x2,y2,test_size=0.3,random_state=4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train2,y_train2)
ypred2=knn.predict(x_test2)
print(ypred2)
y3= data3[' WATER DELIVERIES TO Multi.family.Residential ']
x3=data3[cols1]
x_train3,x_test3,y_train3,y_test3=train_test_split(x3,y3,test_size=0.3,random_state=4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train3,y_train3)
ypred3=knn.predict(x_test3)
print(ypred3)
y4= data3[' WATER DELIVERIES TO Commercial.Institutional ']
x4=data3[cols1]
x_train4,x_test4,y_train4,y_test4=train_test_split(x4,y4,test_size=0.3,random_state=4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train4,y_train4)
ypred4=knn.predict(x_test4)
print(ypred4)
y5= data3[' WATER DELIVERIES TO Landscape.Irrigation ']
x5=data3[cols1]
x_train5,x_test5,y_train5,y_test5=train_test_split(x5,y5,test_size=0.3,random_state=4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train5,y_train5)
ypred5=knn.predict(x_test5)
print(ypred5)
y6= data3[' WATER DELIVERIES TO Other ']
x6=data3[cols1]
x_train6,x_test6,y_train6,y_test6=train_test_split(x6,y6,test_size=0.3,random_state=4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train6,y_train6)
ypred6=knn.predict(x_test6)
print(ypred6)
y7= data3[' WATER DELIVERIES TO Agricultural ']
x7=data3[cols1]
x_train7,x_test7,y_train7,y_test7=train_test_split(x7,y7,test_size=0.3,random_state=4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(x_train7,y_train7)
ypred7=knn.predict(x_test7)
print(ypred7)
import tinder
from tinder import Button
from tinder import Entry
window = tkinter.Tk()
a=knn.predict(x_test)
print(a[0])
CONCLUSIONS
This project attempted to model the expected water usage using KNN in relation to a city's water usage history. The KNN model provided greater accuracy as compared to the neural network. This model showed the overall consumption showing the industry's use of water to understand the necessary supply of water. More in the future, characterization can be considered to build the model more accurately.
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