This paper is published in Volume-8, Issue-2, 2022
Area
Computer Science Engineering
Author
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C.
Org/Univ
Dayananda Sagar Academy of Technology and Management, Udayapura, Karnataka, India
Keywords
Crop Water Demand, Forecast, Crop To Crop Modeling, KNN Modeling, Water Utility Demand Prediction
Citations
IEEE
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C.. Water demand prediction using the KNN algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C. (2022). Water demand prediction using the KNN algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 8(2) www.IJARIIT.com.
MLA
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C.. "Water demand prediction using the KNN algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 8.2 (2022). www.IJARIIT.com.
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C.. Water demand prediction using the KNN algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C. (2022). Water demand prediction using the KNN algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 8(2) www.IJARIIT.com.
MLA
Sourabh Kattimani, Nethra H. L., Bhuvana B., Shashikala C. R., Sachin G. C.. "Water demand prediction using the KNN algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 8.2 (2022). www.IJARIIT.com.
Abstract
Many factors influence irrigation water requirements in an agriculture field. those factors are the age of the plant, type of soil, temperature, level of sunlight, and water needed. Despite the multiple solutions proposed, still the quantity of water overflood and underfloor in the agriculture felid. The artificial influence on irrigation requirement should be thought of as an important impact factor, considering the requirement of water, the technology can help in preserving a large quantity of water in the agriculture felid. development of complex and elaborate forecasting methods such as artificial neural networks (ANNs) can be costly to develop and implement with the limited recourses available.