Rainfall forecast using SARIMA model along the coastal areas of Sindh Province

Rainfall forecasting is critical for economic activities such as agriculture, watershed management, and flood control. It requires mathematical modelling and simulation. This paper investigates the time series analysis and forecasting of the monthly rainfall for the Sindh coastline, Pakistan. The seasonal autoregressive integrated moving average (SARIMA) model was used for the last three decades (1991-2020) and forecasting was done for the next two years. The model is based on the Box Jenkins methodology. The decomposition of time series plots into trend, seasonaland random components showed a seasonal effect. The Augmented Dickey–Fuller (ADF) and Mann–Kendall (MK) tests showed the inherent stationarity of the rainfall data. The best SARIMA models for monthly rainfall were SARIMA (1,0,1)(3,1,1)12 and SARIMA (1,0,1)(1,1,1)12 with Akaike information criterion corrected (AICC) values of 1507 and 1387, respectively. The model predictions indicate that, in the years 2021/22, July will likely have the most rainfall, followed by August and June. The diagnostic statistical test values directed that the adequacy of the models is consistent for projected monthly rainfall forecasts

Enhancing Student Performance Prediction via Educational Data Mining on Academic data

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement

Assessment of underground water resources of Gharo city, Sindh, Pakistan

This investigation focuses on the extent of public health quality of underground water available in Gharo city, Sindh Pakistan that represents a very poor socioeconomic profile. The city has a very limited piped water supply and the people mostly rely on well water. Underground water samples were collected from 28 different locations and the water quality was assessed through a deterministic sampling programme followed by an intense physicochemical and bacteriological analysis. The results of these analyses disclosed that the underground water is grossly polluted due to domestic and agricultural discharges. The problem is further aggravated by poor sanitation conditions. None of the water samples met the water quality criteria set by NSDWQ and WHO. The groundwater was found to be fecally contaminated and poses serious human health hazards. Effective measures are urgently required for water quality management in the city

Impact of indiscriminate disposal of untreated effluents in Korangi creek, Karachi, Pakistan

Abstract Korangi creek is one of the major creek of Indus delta which receives both untreated industrial and domestic effluents. It provides an important waterway to approach Port Qasim. A survey of water quality and the sediments along with biodiversity of benthic fauna was conducted. A total of 24 water and 14 sediment samples were collected from February to November 2014. The mean pH of seawater and sediment samples was 7.41 and 7.5, respectively. The mean salinity of seawater was 36‰. The mean BOD5 of seawater and sediments was 288 mg/l and 1645 mg/kg, respectively, while COD was 1231.9 mg/l and 1645.3 mg/kg, respectively. Cyanide content was low in seawater but slightly higher in sediment. Mean phenol level of seawater and sediment was 0.61 mg/l and 8.11 mg/kg. Heavy metals in the seawater was established to be in the order Pb > Cu > Cr > Ni > Zn > As. The trend in sediment was slightly different and followed the following pattern Pb > As > Ni > Cu > Cr > Zn. The distribution pattern of the estimated variables for seawater and sediments was examined using the principal component analysis and cluster analysis. Annelida and Arthropoda were the dominant components of biodiversity. Taxa diversity was measured and Shannon index (H) ranged between 1.364 and 1.969 while equitability (J) ranged between 0.549 and 0.862. Dominance (D) was in the range of 0.156–0.436

Assessment of heavy metals in unbranded nail polishes retailed in Karachi, Pakistan

This assessment determines the concentrations of arsenic, lead, chromium, nickel, and cadmium in 60 unbranded nail polishes of various colours that were available in local marketplaces in Karachi, Pakistan. The findings demonstrated that mean lead, arsenic, and chromium contents in all the samples fell below FDA-acceptable ranges, and no nail polish colour was discovered to contain levels of these metals that exceeded the upper limits. Similarly, the FDA standards were exceeded by 41.6% of the pink and 25% of the orange nail polish samples. In this investigation, > 3 µg/g Cd was found in nail polish samples of the colours red and pink, respectively, at 66.67% and 8.33%. The order of the mean As, Pb, Cr, Ni, and Cd concentrations in the samples of nail polish that were examined in this study was red>orange>brown>blue>pink; orange>red>blue>pink>brown; red>blue>brown>pink>red; pink>orange>red>blue>brown;  and red>pink>orange>brown>blue. The pink and blue colour samples showed the highest levels of Ni (12.08 µg /g) and Cd (6.28 µg /g)