Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Abstract

Abstract Manzala Lake, the largest of the Egyptian lakes, is affected qualitatively and quantitatively by drainage water that flows into the lake. This study investigated the capabilities of adaptive neuro-fuzzy inference system (ANFIS) to predict water quality parameters of drains associated with Manzala Lake, with emphasis on total phosphorus and total nitrogen. A combination of data sets was considered as input data for ANFIS models, including discharge, pH, total suspended solids, electrical conductivity, total dissolved solids, water temperature, dissolved oxygen and turbidity. The models were calibrated and validated against the measured data for the period from year 2001 to 2010. The performance of the models was measured using various prediction skill criteria. Results show that ANFIS models are capable of simulating the water quality parameters and provided reliable prediction of total phosphorus and total nitrogen, thus suggesting the suitability of the proposed model as a tool for onsite water quality evaluation.