Groundwater is considered a significant component of valuable freshwater resources for the living beings living in the arid and semi-arid regions of Pakistan, factors such as climate change, landfill deposits, application of fertilizers and pesticides on agricultural lands, leakages from septic tanks, industrial effluents, and urbanization jeopardizing the groundwater quality that makes its timely assessment necessary for human survival to protect people from water-borne diseases. This research study was carried out to analyze the 13 physicochemical parameters in the 38 groundwater samples that were taken from nearby different locations of Akram, Pinyari, and Phuleli canal within the jurisdiction of district Hyderabad. 26 samples were taken from hand pumps, electric motors and 12 samples were taken from tube wells to investigate the variability in groundwater quality. The water quality index and kelly’s ratio, magnesium hazard, residual sodium carbonate, sodium absorption ratio, soluble sodium percentage, and permeability index were used to assess the suitability of groundwater for drinking, and agricultural purposes. The result of the study revealed that alkalinity, bicarbonates, carbonates, magnesium, PH, potassium, and sodium were within the permissible limit of WHO Standards in all the samples, while the concentration of calcium was crossing the permissible limit only in one sample. Moreover, the availability of Chloride (Cl) was found in 16 samples that were above the limit ranges from 274.9 to 2549,  High concentration of EC was found in 14 samples than the permissible limit having values from 2212 to 8360 (P26), and Total Hardness (TH) were found only in 6 samples slightly high from the permissible limit ranges from 509 to 651, and TDS were present in excessive amounts than the allowable limit from 1100 to 4180 mg/l (P26) in 10 samples. Considering the Water Quality Index (WQI) it was observed that 8 sample falls in the good category, 2  samples in the poor category, 11 samples in the very poor category, and 17 samples in the unsuitable category of water quality.

Modeling of flow discharge plays a significant role in effective planning, sustainable usage, development, and management of water resources in short (hourly) and long-term (monthly) temporal categories. Since the inception of managing water resources, various techniques such as conceptual, metric, and physical models have been introduced all of these require a large amount of data, labor, and expense to be incorporated to obtain reliable results, due to which Artificial Intelligence methods were introduced that require less amount of data, time, expense and as well as experience to model flow discharge. In this research study, an attempt was made by employing two different artificial neural network techniques feedforward neural networks (FFNN), and time-lagged neural networks (TLNN) to model and predict the river flow discharge at daily and monthly timescale. 2010 and 503 no. of observations were used for model calibration and validation in daily time scale while 557 and 139 observations were used in monthly timescale. The result of the study revealed that the FFNN modeling approach has captured the daily and monthly stream flow variability very well than the TLNN model with R2 of 0.91 on the daily and 0.71 on the monthly time scale while R2 for the TLNN model was 0.79, and 0.34 for daily and monthly timescale. This indicates that the FFNN technique requires less no. of observations and is more reliable than TLNN and can be used to model river flow.