AI based Waterfoot Print of Crop

This research studies analyzes the essential of water footprint data in numerous geospatial applications like water footprint modeling with water footprint of crop unit. It conducts a through regression analysis on water footprint data utilizing Support Vector Machines (SVM), Polynomial, Linear Regression(LR), Random Forest(RF), Decision Tree (DT) and AdaBoost algorithms. The study aims to assess their effectiveness in predicting water footprint values across water footprint of crop unit. By utilizing a diverse water footprint dataset from various geographical locations, the study covers various preprocessing techniques not within dataset cleaning, scaling of feature, and partitioning of datasets. Every algorithm is utilized on the training dataset to generate regression models, and are assessed using testing set. The algorithm gives the root means square and mean square and R - square is utilized for validating the algorithms.