Advance Ensemble Flood Warning System: A Case Study for Nullah Lai

River flow forecasting is an essential tool to manage floods in the current era, especially for flash flooding scenarios in urban areas. This study focuses the flash flooding scenario in the Nullah Lai basin, which comprises the twin cities Islamabad and Rawalpindi. Steep slopes in the Margalla hills and Islamabad create high numbers of flash floods in the lower reaches of Rawalpindi, which are densely populated. When high-intensity rainfall occurs in the steep slopes of Margalla and Islamabad, high-volume floods with high velocity pour down, which instantaneously reaches the less-sloped Rawalpindi regions, which causes the raising of the water level in the stream, and flooding occurs. The section of the Nullah Lai Rawalpindi starting from the Qatarian bridge to the Gawalmandi bridge has always faced flash flooding over time. In the period of few hours, the water level reaches several fts in the nullah, which is why it is not possible to alert the people living on the banks in a timely manner, a problem that illuminates the need for a forecasting system at Nullah Lai. In the current research, the China Metrological Agency forecast center (CMA)’s ensemble forecast data have been utilized to achieve forecasts in the Nullah Lai. For this purpose, two initial objectives were set to achieve which basic needs are required process the data available in grib format from data centers. A digital model of the Nullah Lai was made using hydrology tools available in ArcGIS 10.3. A digital equation was obtained from gene expression modeling (GEP), which was later used to generate the ensemble stage forecast against the ensemble rainfall forecast. The results obtained show that the flash flooding phenomenon in Nullah Lai can, with some uncertainty, be predicted well in time. Using 3-days-ahead forecast data from CMA, the same floods were predicted 3 days before the event. This research also provides the procedure to use the ensemble forecast data in developing an automated model to generate the ensemble stage forecast for coming events. This study will help the administrative authorities better manage the upcoming floods and save lives and capital costs lost in the flash flooding phenomena which continuously happen in the basin of the Nullah Lai

An Organizational-Based Model and Agent-Based Simulation for Co-Traveling at an Aggregate Level

Carpooling is an environmentally friendly and sustainable emerging traveling mode that enables commuters to save travel time and travel expenses. In order to co-travel, individuals or agents need to communicate, interpret information, and negotiate to achieve co-operation to find matching partners. This paper offers the scheme of a carpooling model for a set of candidate carpoolers. The model is interpreted using an agent-based simulation to analyze several effects of agents’ interaction and behavior adaptations. Through communication and negotiation processes, agents can reach dynamic contracts in an iterative manner. The start of the negotiation process relies on the agents’ intention to emit an invitation for carpooling. The realization of the negotiation process depends significantly on the departure time choice, on the agents’ profile, and on route optimization. The schedule or agenda adaptation relies on the preferences among the realistic schedules of the agents and usually depends on both the participation of the trip and on the time of day. From the considerations, it is possible to reveal the actual representation of the possible carpoolers during the simulated period. Experiments demonstrate the nearly-polynomial relationship between computation time and the number of agents

Dynamics of Water Quality: Impact Assessment Process for Water Resource Management

Surface water is an important source of water supply for irrigation purpose and in urban areas, sewage water is being disposed of in nearby canals without treatment. A study was conducted to investigate the dynamics of water quality of irrigation canal as a result of this practice. The study ascertained the impact of different salinity parameters, indices and approaches to examine the hazardous effects on quality of canal water. The study analyses the samples collected for various parameters like pH, TDS, EC, Na, Cl, Ca, Mg, K, CO3, HCO3 etc. It helped to decide the restriction on use of water based on FAO-UN guidelines. Investigations were focused on assessment of contaminants affecting the quality of water and having hazardous effects on different stages of irrigation water usage. Wilcox diagram and Doneen’s approach-based analysis helped to identify the class and quality of water. This study shall help to analyze the quality of water and provide support to the decision makers for better water resource management and policy development for irrigation purpose i.e., treatment and distribution of water resource

Analysis of Mechanical Properties of Self Compacted Concrete by Partial Replacement of Cement with Industrial Wastes under Elevated Temperature

Self-Compacting Concrete (SCC) differs from the normal concrete as it has the basic capacity to consolidate under its own weight. The increased awareness regarding environmental disturbances and its hazardous effects caused by blasting and crushing procedures of stone, it becomes a delicate and obvious issue for construction industry to develop an alternative remedy as material which can reduce the environmental hazards and enable high-performance strength to the concrete, which would make it durable and efficient for work. A growing trend is being established all over the world to use industrial byproducts and domestic wastes as a useful raw material in construction, as it provides an eco-friendly edge to the construction process and especially for concrete. This study aims to enlighten the use and comparative analysis for the performance of concrete with added industrial byproducts such as Ground Granulated Blast Furnace Slag (GGBFS), Silica fumes (SF) and Marble Powder (MP) in the preparation of SCC. This paper deals with the prediction of mechanical properties (i.e., compressive, tensile and flexural Strength) of self-compacting concrete by considering four major factors such as type of additive, percentage additive replaced, curing days and temperature using Artificial Neural Networks (ANNs)