Compared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost

solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points

of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic

disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase

approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized

in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the

location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate

levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified

number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the

second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to

minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels

of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of

chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective

genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different

numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can

explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the tradeoff

between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels.

Runoff simulation is a vital issue in water resource planning and management. Various models with different levels of accuracy

and precision are developed for this purpose considering various prediction time scales. In this paper, two models of IHACRES

(Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) and ANN (Artificial

Neural Network) models are developed and compared for long term runoff simulation in the south eastern part of Iran. These

models have been utilized to simulate5-month runoff in the wet period of December-April. In IHACRES application, first the

rainfall is predicted using climatic signals and then transformed to runoff. For this purpose, the daily precipitation is downscaled

by two models of SDSM (Statistical Downscaling Model) and LARS-WG (Long Ashton Research Station-Weather Generator). The

best results of these models are selected as IHACRES model input for simulating of runoff. In application of the ANN model,

effective large scale signals of SLP(Sea Level Pressure), SST(Sea Surface Temperature), DSLP and runoff are considered as model

inputs for the study region. The performances of the considered models in real time planning of water resources is evaluated by

comparing simulated runoff with observed data and through SWSI(Surface Water Scarcity Index) drought index calculation.

According to the results, the IHACRES model outperformed ANN in simulating runoff in the study area, and its results are more

likely to be comparable with the observed values and therefore, could be employed with more certainty.