Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models

Abstract

Egyptian coastal lakes, four lakes, and two lagoons represent about 25% of the Mediterranean total wetlands, and the four lakes are located at the north of Nile Delta and are known as Northern Delta Lakes. Manzala Lake, the largest of the Egyptian lakes, is affected qualitatively and quantitatively by drainage water that flows into the lake. In water quality modeling, deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In some cases, the deterministic models have high chance of failure due to absence of prior information. A deterministic model may also have inevitably errors originated from model structures or other causes. For such cases, new modeling paradigm such as data-driven modeling or data mining has recently been a considerable growth in the development and application of computational intelligence and computer tools with respect to water-related problems. This chapter illustrates the capabilities of adaptive neuro-fuzzy inference system (ANFIS) to predict water quality parameters in Manzala Lake based on water quality parameters of drains associated with the Lake. 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, salinity, and turbidity. The models were calibrated and validated against the measured data. The performance of the models was measured using various prediction skills criteria. Results show that ANFIS models are capable of simulating the water quality parameters and provided reliable prediction of total phosphorus and total nitrogen and, thus, suggesting the suitability of the proposed model as a tool for on-site water quality evaluation.