Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.

Urbanization poses a significant threat to environmental sustainability, particularly in Pakistan, where uncontrolled urban growth and water mismanagement have exacerbated water scarcity and climate variability. This study investigates the spatiotemporal impacts of urbanization and climate change on groundwater in Lahore District, Pakistan. various parameters were considered to execute the study including land use/land cover (LULC), rainfall, Land Surface Temperature (LST), ground wells and population data using advanced techniques such as Random Forest machine learning algorithm, Climate Hazards Group Infrared Precipitation, and geographically weighted regression (GWR) analysis. Our findings reveal that urbanization has severely impacted the water table in the north, northwest, and southwest areas. There is a significant negative negative correlation (−0.333) between the quantity of groundwater level (GWL) and the annual average LST whereas, the p-value (0.75) is also showing highly significant relation of GWL and LST in the study area. Whereas a positive association (0.666) exist (p-value 0.333 moderately significant) between yearly GWL and the mean precipitation. This research provides crucial insights for policymakers to understand the effects of urbanization and climate change on groundwater and develop strategies to mitigate adverse impacts in the study area.

Pakistan is prone to natural hazards including floods, in particular, affecting millions of people each year across the country. Patterns from recent years suggest the intensity of flooding are increasing and may continue more aggressively during the coming years because of increased magnitude of the monsoon rains and identified climatic changes in the region. Mapping of flood hazard is essential for planning and mitigation purpose. However, only flood hazard mapping is not sufficient to assess the magnitude of risk to lives and property. Risk mapping is an integral part of pre and post disaster management. Furthermore, the Earth Observation (EO) data could be helpful to update flood risk maps time to time by covering many aspects e.g. population concentration areas, critical infrastructure and commercial areas, to enhance the preparedness planning and mitigation measure against risk of flood. Pakistan has experienced the devastating flood in 2010 due to unexpected heavy rainfall in the monsoon season with enormous losses to property lives and infrastructure in several districts. The Union council Agra, District Charsadda was among the affected most districts by riverine and flash floods. Additionally, growing population in flood plains is another threat to the district. However, to cope with this situation there is immense need to detect risk index to prevent further damages caused by such floods. This study aims to prepare the hazard, vulnerability and risk index for River Kabul and Swat catchment area of union council Agra, Charsadda. the flood risk was identified, and risk index maps were prepared by executing a methodology for assessing risk, based on the physical exposure of the flood hazard, vulnerability of people, and the exposure of critical assets to flood water. Explicitly, spatial flood risk index maps were produced with the help of analytical spatial modeling by considering the areas exposed to flood hazard, morphological characteristics and socio-economic indicators. The produced flood risk maps were verified through visual examination through 3D city flood maps. Results illustrate that the areas of higher flood risk overlapped with the areas of high flood hazard along with high population density and socio-economic exposure to vulnerability.

Keywords: Landslide, GIS, Susceptibility, Fuzzy logic, Pakistan Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.