International Journal of Innovations in Science & Technology
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Automated Seismic Horizon Tracking Using Advance Spectral Decomposition Method
Get PDFIntroduction/Importance of Study: In three-dimensional seismic interpretation, automatic horizon tracking is a critical productivity tool. However, it often fails in areas where horizons are not smooth and exhibit sharp discontinuities such as large spatial displacement or changes in reflector aliasing, horizon gradients, and signal character. Such failures require manual intervention, which increases the interpretation cycle time.
Novelty Statement: In this research study, an automated horizon tracker is proposed that adapts to changes in reflector shape, strength, and geological variation as it traverses through the seismic data volume.
Material and Method: A predefined spatial grid window steers across the horizon surface where its orientation changes with the variation in a pre-computed, high-resolution, dip volume. The method is further improved to incorporate tracking horizons across discontinuities i.e. faults.
Result and Discussion: The proposed method is tested on three-dimensional seismic data with varying geological conditions and has demonstrated successful mapping of horizon surfaces and effective matching across major faults.
Concluding Remarks: Our automatic procedure, by reducing the need for manual intervention during interpretation, has the potential to significantly improve productivity
Assessment Of Groundwater Quality Index For Agriculture And Domestic Purpose Of Taluka Sehwan, District Jamshoro
Get PDFIntroduction/Importance of study
Groundwater has become an important source of freshwater around the world, used for a variety of reasons such as home usage, agricultural irrigation, and industrial applications.
Novelty statement
This study provides a novel solution by using the water quality index (WQI) and GIS-based Kriging analysis to comprehensively assess and spatially visualize groundwater quality in Taluka Sehwan, Sindh, Pakistan, addressing the critical issue of contamination from Manchar Lake.
Material and Method
Thirty groundwater samples were collected from Taluka Sehwan, Sindh, Pakistan, and sixteen parameters, including pH, electrical conductivity (EC), and total dissolved salts (TDS), were analyzed in the lab. The water quality index (WQI) and irrigation indices (SAR, SSP, MH, and PI) were calculated, and the results were spatially analyzed using the GIS-based Kriging method.
Result and Discussion
The WQI in the study area ranges from 34.53 to 213.362, with only 13% of the water deemed good, 23% poor, 7% very poor, 30% unsuitable, and 27% unfit. The overall WQI indicates that the groundwater is unsafe and non-potable, except for a few localized pockets (13%) in the northern side. SSP was categorized as unsure (83.33%) or poor (13.33%) for irrigation. SAR values indicated that 10% of the water is excellent, 46.67% good, 33% allowable, and 10% unsuitable for agriculture. MH and PI indices showed 70% of the water as excellent and 30% as safe. The water quality is poor, with moderate to good irrigation indices suitable for 70-75% of the area. Spatial analyses reveal low concentrations in the north and high concentrations in the south, highlighting the area\u27s heterogeneity.
Concluding Remarks
The policy should prioritize monitoring pollution, research on sources, and mitigation methods to prevent irreversible harm to the local ecosystem and communities
Validation of Satellite-Based Gridded Rainfall Products with Station Data Over Major Cities in Punjab
Get PDFA Critical evaluation of newly developed gridded rainfall datasets is essential for their effective application. Over the past two decades, the availability of gridded rainfall measurements has increased; however, finding suitable proxies for traditional station-based measurements remains challenging. This study conducted a comparative assessment of rainfall estimates from IMERG, CHIRPS, ERA-5, and APHRODITE against meteorological station data from five cities in Pakistan: Lahore, Faisalabad, Multan, Islamabad, and Murree. The assessment covered multiple temporal scales (daily, monthly, and yearly) using daily data recorded from 2001 to 2022. Analytical metrics applied included Bias, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Coefficient of Determination (R²). The results revealed notable spatial and temporal patterns of agreement among the datasets. Correlations for daily data were generally weak across all gridded datasets, with APHRODITE performing the best. Monthly aggregates showed that IMERG had the highest association with ground data, followed by CHIRPS. Yearly accumulated rainfall records indicated that IMERG had the highest correlation, followed by CHIRPS. Overall, IMERG demonstrated higher consistency across stations at both monthly and yearly scales. CHIRPS exhibited lower errors (RMSE and bias) at most locations, especially Lahore, but showed higher errors in Murree at the monthly scale. The study concludes that a single satellite dataset alone may not provide sufficient accuracy over large areas; a combination of products may be required for better estimation
Gemstones Supply Chain Management through Blockchain Mechanism
Get PDFThe provenance of gemstones significantly enhances their value. However, both conventional supply chain management and digital systems are susceptible to counterfeiting, loss, and theft. Blockchain has emerged as a suitable technology to store tamper-proof records of gemstones allowing the storage of immutable journey of gemstones. This research article shows how the blockchain-based Ethereum network can be used for managing the supply chain of gemstones. Mining details, cutter information, digital certificates, proof of ownership, quality, and sales history of gemstones can be arranged in a two-tiered blockchain network to allow multiple organizations to securely share specific information within the organization and publicly. We cover the major supply chain exchanges for gemstones and end users with Ethereum smart contracts. We present that our suggested decentralized architecture-based solution can overcome many limitations in terms of immutability, traceability, verifiability, and security which exist in both conventional and digital supply chain management systems. Test scripts or smart contracts are publicly deployed on the Ethereum network
Assessment of Three Fast Growing Populus Deltoides Species in Various Soil Profiles Under Nursery Conditions
Get PDFPopulus plants are fast-growing plants exhibiting strong adaptability and a short rotation period, with enhancing ability of carbon stock, helping in combating climate change and sustaining livelihoods. Pakistan has a shortage of firewood and timber. Thus, hybrid fast-growing plants are the only way to balance wood demand and supply in country. Therefore, objective of the present study was to evaluate and compare the growth patterns and carbon stocks of Populus deltoides varieties in soil media under nursery conditions. To achieve this, three fast-growing hybrid species of Populus deltoides, Italian Populus (euramerciana), clone A-Y48, and local Populus were used. Three healthy plants of mothers aged one to two years were selected from field area of the Rangeland Research Institute, NARC. The cuttings were planted in 90 pots after being filled with three different media, and plant growth was recorded after seven days for the number of leaves, height, diameter, and irrigations frequency applied to each pot. Three-month data were collected and analyzed by using an RCBD design. Afterwards, all the plants were harvested, and soil samples were taken from the pots and brought to the RRI laboratory for estimation of total biomass and carbon stocks. It was concluded that Clone AY-48 achieved highest height among all Populus deltoides varieties and stored more carbon stock in comparison to Italian and local poplar varieties. Farmyard manure had a positive influence on height of the different Populus deltoides varieties. Clone AY-48 and Italian poplar plants are more suitable for rapid growth
Designing Flood Risk Reduction Plan for Kalat Division, Balochistan
Get PDFFlood risk mitigation is crucial in the Kalat Division of Balochistan Province due to frequent flooding events that endanger lives and infrastructure. This study introduces a novel approach by integrating Landsat 8-9 OLI data with advanced remote sensing techniques to address flood risks in the region, an approach not previously utilized. Covering the period from 2015 to 2022, the research employs satellite imagery and indices such as NDWI, MNDWI, NDVI, LULC, and Watershed Analysis, with thorough pre-processing to ensure data accuracy. NDWI and MNDWI analyses effectively mapped and monitored water bodies, pinpointing vulnerable areas essential for flood risk assessment. NDVI analysis revealed significant correlations between vegetation dynamics and flooding, highlighting ecological impacts. LULC analysis identified substantial changes in land use patterns, emphasizing the role of human activities in flood vulnerability. Watershed Analysis offered valuable insights into hydrological dynamics and precipitation patterns, supporting flood prediction and mitigation efforts. This integrated approach provided a comprehensive understanding of climatic, hydrological, and land cover factors contributing to flood vulnerability, enabling the development of evidence-based flood risk management strategies. The findings enhance the Kalat Division\u27s resilience against future floods through informed, evidence-based mitigation strategies
Designing an AI-Based Greenhouse Plant Monitoring System to Detect and Classify Plant Diseases from Leaf Images
Get PDFPlant diseases can significantly hinder food crop production, leading to substantial economic losses and posing a threat to global food security. Machine learning, particularly deep learning, plays a crucial role in object detection and classification. In this study, we present an AI-based plant monitoring system for detecting and classifying plant diseases using visual images. Our deep learning models are trained on plant images obtained from natural environments. Manual detection and classification are both challenging and labor-intensive, making accurate and timely diagnoses from an automatic system highly beneficial for treating plant diseases. Traditionally, plant disease detection using deep learning has relied on images taken in controlled environments, which do not support in-situ detection for remote monitoring. The Plantdoc dataset, a popular resource consisting of plant images from actual field conditions, is used in our study. We employ the YOLOv5 algorithm from the field of computer vision to the Plantdoc dataset, achieving results that surpass previous work on the same dataset. This success is attributed to our selected model and data augmentation techniques. Our model can classify and detect various diseased and healthy leaf classes with a mean Average Precision (mAP) of 92%. This capability enables farmers and researchers to remotely monitor plant health and diagnose plant diseases, thereby saving time, reducing costs, and minimizing crop loss
Analyzing Government Policies Causing Smog: An Evaluation
Get PDFIntroduction/Importance of Study: This study aims to explore the relationship between government policies and the worsening of smog through a comprehensive analysis. Understanding this relationship is essential for designing policies that effectively reduce air pollution, particularly smog.
Novelty Statement: This research introduces a novel approach by proposing a practical solution to address the worsening smog issue, highlighting the gap between government policies and their on-ground implementation.
Material and Method: We conducted an extensive review of relevant laws, policies, regulations, and jurisprudence related to environmental protection. Environmental protection measures were identified through provincial and national environmental protection department websites. Actions required for environmental protection, as outlined in these legal documents, were assessed to develop a viable solution.
Result and Discussion: This study aims to make a significant contribution toward achieving a \u27Good\u27 Air Quality Index (AQI) and guiding the creation of effective government policies. The goal is to ensure that policies are balanced—not too lenient or too strict—so they can address real-time issues effectively.
Concluding Remarks: Achieving an optimal solution requires collaboration among legislators, researchers, technocrats, academicians, and the executive branch. Furthermore, any deficiencies in policy implementation should be addressed by the High Courts or the Supreme Court to ensure accountability and effectiveness
Change Detection of Land Cover Using Geo-Spatial Techniques in District Hyderabad, Sindh, Pakistan
Get PDFUrban expansion worldwide is leading to significant changes in land cover, with built-up areas increasingly encroaching on agricultural and barren lands. This study utilizes geospatial techniques to analyze land cover changes in District Hyderabad, Sindh, Pakistan, from 2013 to 2023, marking a pioneering effort in this region. Landsat images from 2013, 2018, and 2023, sourced from the US Geological Survey database, were analyzed using the maximum likelihood technique of supervised image classification. Four major land cover classes—vegetation cover, built-up land, water bodies, and barren land—were identified. The analysis reveals a notable increase in built-up areas, rising from 41% in 2013 to 68% in 2023. In contrast, vegetation cover has decreased by 14%, water bodies by 6%, and barren land by 7% over the past decade. These changes indicate Hyderabad\u27s shift from a rural to an urban landscape, driven by socioeconomic development. The findings underscore the importance of sustainable development practices that reconcile urban growth with environmental preservation. This study provides essential data for urban planning, conservation efforts, and further research on land cover dynamics
Monsoon 2022 Floods and Its Impacts on Agriculture Land Using Geospatial Approaches: A Case Study of Khyber Pakhtunkhwa Province Pakistan
Get PDFCountry of Pakistan is at higher risk because of climate change. The country affected by the extreme heat wave happened in May followed by devastating flood disaster in august 2022. Pakistan faced numerous disasters in near past. Pakistan faced a very high magnitude of earthquake round about 7.6 in between 2010 and 2014, in addition in 2022 we were the victims of severe floods across the country. Such events have an adverse effect on the financial figures of the country. Floods affect the whole province Khyber Pakhtunkhwa Pakistan but 09 districts were severely affected by the monsoon flood 2022. In southern districts DI khan (Dera Ismail khan) and Tank district were severely affected by the floods. In northern districts swat, Dir lower, Dir upper and some areas of District Chitral were affected. In the central Division District Nowshera, Charsadda and Peshawar were affected by the recent floods. Filed data were collected and bring them to Geospatial format for further analysis and then physically verified the damages of floods 2022. The results disclosed that in DI khan division, District DI khan and sub division DI khan recent monsoon spell damaged 1377.544215 Sq.km crop area, the cropped area damaged fully or partially in district tank and sub division tank is 270.146935 sq.km. In Peshawar division the crop land of District Charsadda were badly damaged where the sugarcane and maize crops were affected. Area calculated using GIS in district Charsadda that was damaged is 117.555732 Sq.km. In district Nowshera a total of 467.744999 and 30.081483 sq.km crop area were damaged in District Peshawar. The northern part of Khyber Pakhtunkhwa occupies hilly area and the residents relies on agriculture practices and gardening. In Malakand division district swat was much affected by the current spell of Monsoon where 122.38179 sq.km area of main crop and orchards were damaged. Similarly, 63.603461 sq.km area of District lower Dir, 15.147068 sq.km of upper Dir and 575.678 sq.km crop land of District Chitral were hit by the floods happened in 2022