Qanat Discharge Prediction Using a Comparative Analysis of Machine Learning Methods

Abstract

The Qanat (also known as kariz) is one of the significant water resources in many arid and semiarid regions. The present research aims to use machine learning techniques for Qanat discharge (QD) prediction and find a practical model that predicts QD well. Gene expression programming (GEP), artificial neural network (ANN), group method of data handling (GMDH), least-square support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS), are employed to predict one-, two-, and five-months time step ahead QD in an unconfined aquifer. QD for one, two, and three lag-times (QD_t−1, QD_t−2, QD_t−3), QD for adjacent Qanat, the main meteorological components (T_t, ET_t, P_t) and GWL for one, two, and three lag-times are utilized as input dataset to accomplish accurate QD prediction. The GMDH model, according to its best results, had promising accuracy in predicting multi-step ahead monthly QD, followed by the LSSVM, ANFIS, ANN and GEP, respectively.