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Storage dams play a very important role in irrigation especially during lean periods. For proper regulation one should make sure the availability of water according to needs and requirements. Normally regression techniques are used for the estimation of a reservoir level but this study was aimed to account for a non-linear change and variability of natural data by using Gamma Test, for input combination and data length selection, in conjunction with Artificial Neural Networking (ANN) and Local Linear Regression (LLR) based models for monthly reservoir level prediction. Results from both training and validation phase clearly indicate the usefulness of both ANN and LLR based prediction techniques for Water Management in general and reservoir level forecasting in particular, with LLR outperforming the ANN based model with relatively higher values of Nash-Sutcliffe model efficiency coefficnet (R 2 ) and lower values of Root Mean Squared Error (RMSE) and Mean Biased Error (MBE). The study also demonstrates how Gamma test can be effectively used to determine the ideal input combination for data driven model development.

Rainfall–runoff is a complicated nonlinear process and many data mining tools have demonstrated their powerful potential in its modelling but still there are many unsolved problems. This paper addresses a mostly ignored area in hydrological modelling: data time interval for models. Modern data collection and telecommunication technologies can provide us with very high resolution data with extremely fine sampling intervals. We hypothesise that both too large and too small time intervals would be detrimental to a model's performance, which has been illustrated in the case study. It has been found that there is an optimal time interval which is different from the original data time interval (i.e. the measurement time interval). It has been found that the data time interval does have a major impact on the model's performance, which is more prominent for longer lead times than for shorter ones. This is highly relevant to flood forecasting since a flood modeller usually tries to stretch his/her model's lead time as far as possible. If the selection of data time interval is not considered, the model developed will not be performing at its full potential. The application of the Gamma Test and Information Entropy introduced in this paper may help the readers to speed up their data input selection process.

Almost in all hydrological models, calibrating parameters are tuned to best match the simulated results with the observed. In the present study sensitivity analysis was carried on the fifteen calibrating parameters of University of British Columbia Watershed Model (UBCWM). The study focuses to impart information to the modelers while calibrating UBCWM. To achieve the objectives of the study, UBC Watershed Model was applied on Chitral watershed in Pakistan. UBC Watershed Model is a semi distributed Hydrological model which divides the entire watershed in several elevation bands. The model was calibrated for the year 2006 with the coefficient of efficiency as well as the coefficient of determination equal to 0.94. The numerical values of the calibrating parameters were changed by increasing 20% and then by decreasing 20% of the standard calibrated values one by one. Sensitivity of the model was evaluated by computing the Absolute Sensitivity Index for each parameter. The sensitivity analysis results showed that the P0SREPO as the most sensitive parameter with 3.31525 Absolute Sensitivity Index (ASI) whereas C0IMPA found to be least sensitive giving a value of 0.0452 as Absolute Sensitivity Index (ASI). The logical trends in the results of sensitivity analysis show the robustness of the model.

Glaciers are also known as solid reservoirs, and in this regard, Pakistan is a blessed country to have enriched glaciers. The change in glacial extent becomes very crucial for rivers whose discharges are associated with glacier melt. Even a little change in the glacial extent may bring a significant change in the resulting river flows. Considering climate change scenarios, many researchers have predicted future flows in such catchments. But in almost all such studies, the reduction in the glaciers is not normally based on any rational. Therefore, research is needed in order to estimate how glaciers are actually behaving under the change of temperature and precipitations to better estimate the future flows. For this purpose, Chitral watershed was considered as the study area. The seasonal change in the snow extent was estimated by using MODIS data for various years that helped to identify the month with minimum glacial extent. With the help of remote sensing, unsupervised classification was performed to estimate the glacier area in Chitral watershed. The results show a definite receding trend with respect to time in the glaciers of the region for the past decade.

Population increase and climate change are stretching not only the world’s but also Pakistan’s water resources. This has directly been responsible for the recurring patterns of floods and droughts in the country which emphasizes the importance of the fact that efficient practices need to be adopted for water resource sustainability. This study investigates the use of upland catchment information, comprising of hydrometeorological datasets for inflow prediction to the Tarbela reservoir (a multipurpose reservoir located on River Indus) using Artificial Neural Networks (ANN) and Regression Techniques (Standard and Step Wise). Input Combination and data length selection for all the selected techniques were performed with the aid of Gamma test (GT). This study has made a significant contribution for future water resource management within the Indus Basin as Tarbela is the main source of irrigation, water supply and hydropower generation in Pakistan along with flood control.

Modelling in ungauged catchments has always been a complicated issue due to scarcity of the requisite data. Therefore, hydrologists have to rely on indirect techniques of estimation. A novel approach is hereby presented for solar radiation estimation in ungauged catchments using readily available datasets of temperature and precipitation. The rationale is that the extraterrestrial radiation is attenuated not only by different atmospheric processes but also by weather phenomena. A comparison of four clear sky radiation models was made and the best model output together with temperature and precipitation data was analysed using the gamma test for best input combination and data length selection. This led to the development of a non-linear artificial neural network model for solar radiation estimation. Results show a good correlation between the observed and estimated values, depicting the usefulness of gamma test in model development. The study is novel in the sense that this is the first time the gamma test has been used for hourly solar radiation estimation in ungauged catchments and hence extends their solar radiation records. Moreover, the regionalisation of the proposed model to other catchments would also aid solar radiation estimation in such catchments.

This article looks at the current status and evolution of management of peritoneal surface malignancies in Pakistan. A brief overview of health-care infrastructure in the country and an outline of the centers actively involved in the management of PSM are provided.

The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.

Plastic waste accumulation has been recognized as one of the most critical challenges of modern societies worldwide. Traditional waste management practices include open burning, landfilling, and incineration, resulting in greenhouse gas emissions and economic loss. In contrast, emerging techniques for plastic waste management include microwave-assisted conversion, plasma-assisted conversion, supercritical water conversion, and photo reforming to obtain high-value products. Problems with poorly managed plastic waste are particularly serious in developing countries. This review article examines the emerging strategies and production of various high-value-added products from plastic waste. Additionally, the uses of plastic waste in different sectors, such as construction, fuel production, wastewater treatment, electrode materials, carbonaceous nanomaterials, and other high-value-added products are reviewed. It has been observed that there is a pressing need to utilize plastic waste for a circular economy and recycling for different value-added products. More specifically, there is limited knowledge on emerging plastic waste conversion mechanisms and efficiency. Therefore, this review will help to highlight the negative environmental impacts of plastic waste accumulation and the importance of modern techniques for waste management.

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.