Visualisation of Uncertainty in 30m Resolution Global Digital Elevation Models: SRTM v3.0 and ASTER v2

Geospatial visualisation presents us with innovative techniques of assessing uncertainty in digital elevation datasets. It gives the viewer immediate feedback on potential problems and heightens understanding of effects not easily appreciated when dealing with numerical statistics only. This study evaluated the performance of 30-metre resolution SRTM version 3.0 and ASTER GDEM version 2 over Lagos, Nigeria. Both datasets were examined by direct comparison with 176 highly accurate Ground Control Points (GCPs) coordinated by GPS (Global Positioning System) observation. The basis of comparison was on the elevation differences between the Digital Elevation Models (DEMs) and the GCPs at coincident points. The performance of both DEMs was visualised in 2D and 3D space by comparing pixel values and surface models. In the assessment, the absolute vertical accuracies of SRTM v3.0 and ASTER v2 are 4.23m and 28.73m respectively. The accuracy of SRTM for the study site proved to be higher than the value of 16m presented in the original SRTM requirement specification. ASTER did not meet up with its 17m overall accuracy specification.KEYWORDS: Uncertainty, Visualisation, Digital Elevation Model, SRTM, ASTER

MODELING AND OPTIMIZATION OF WATER-JET TRANSPORT PHENOMENON IN FIRE SERVICE

A model is developed that will allow the fireman to stand as far back as possible from a collapsing wall of a storey building while directing a water jet into a window of the burning building. The variables in the model are therefore, the initial angle (α) and the distance of the fireman from the wall (x). Data collected from Imo State Fire Service, Nigeria, were used in validating the model. The model gives 60o as an optimum initial water jet angle to the horizontal. With 60o as the initial angle, the optimum distance is dependent on the initial velocity of the gun. This can be applied in a burning storey building that is in the risk of collapsing anywhere in the world. The optimum distance from the wall must satisfy equation (10). The work enables the fireman to know a particular point to stand near the building with respect to initial velocity of water fountain and its initial tilt to the horizontal

Kinetic Modeling of Mango Fruit Ripening

In this work, three stages of mango ripening (mango ripening, ethylene inducement, rotting) are modelled kinetically. Data for mango ripening are obtained from internet, and are used to perform regression analysis of the kinetic models developed. It is seen that the results show linear relationship between concentrations and conversion for all the chemical components in all the models (fig. 1a, 2a, 3a). Also the results of the concentration-time relationship are highly non-linear (fig. 1b, 2b and 3b). The reciprocal of the reaction rates varies non-linearly with conversion: profile of ethylene inducement and rotting rise exponentially while that of mango ripening falls non-linearly. The result of this study will help those dealing with fruits in orchard during harvesting and post harvest handling

THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION

Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users

Comparison of machine learning and statistical approaches for Digital Elevation Model (DEM) correction: interim results

The correction of digital elevation models (DEMs) can be achieved using a variety of techniques. Machine learning and statistical methods are broadly applicable to a variety of DEM correction case studies in different landscapes. However, a literature survey did not reveal any research that compared the effectiveness or performance of both methods. In this study, we comparatively evaluate three gradient boosted decision trees (XGBoost, LightGBM and CatBoost) and multiple linear regression for the correction of two publicly available global DEMs: Copernicus GLO-30 and ALOS World 3D (AW3D) in Cape Town, South Africa. The training datasets are comprised of eleven predictor variables including elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector ruggedness measure, percentage bare ground, urban footprints and percentage forest cover as an indicator of the overland forest distribution. The target variable (elevation error) was derived with respect to highly accurate airborne LiDAR. The results presented in this study represent urban/industrial and grassland/shrubland/dense bush landscapes. Although the accuracy of the original DEMs had been degraded by several anomalies, the corrections improved the vertical accuracy across vast areas of the landscape. In the urban/industrial and grassland/shrubland landscapes, the reduction in the root mean square error (RMSE) of the original AW3D DEM was greater than 70%, after correction. The corrections improved the accuracy of Copernicus DEM, e.g., > 44% RMSE reduction in the urban area and >32% RMSE reduction in the grassland/shrubland landscape. Generally, the gradient boosted decision trees outperformed multiple linear regression in most of the tests

Modeling The Mechanism Of Carbon Capture And Sequestration (Ccs) In A System

Rate of carbon sequestration or annual uptake was modeled. Data from Mississippi Delta, ponderosa pine and black walnut, all in USA were used to validate the models. The co-relations of these models for these three sources of data were very high, suggesting that carbon sequestration is modelable and predictable provided that there is a perfect experimental method to capture and sequester the carbon compound with time. This work is a stepping stone to solving carbon capture and sequestration problem of our planet earth. Through a global engineering and technology it is feasible

Performance analysis of Bayesian optimised gradient-boosted decision trees for digital elevation model (DEM) error correction: interim results

Gradient-Boosted Decision Trees (GBDTs), particularly when tuned with Bayesian optimisation, are powerful machine learning techniques known for their effectiveness in handling complex, non-linear data. However, the performance of these models can be significantly influenced by the characteristics of the terrain being analysed. In this study, we assess the performance of three Bayesian-optimised GBDTs (XGBoost, LightGBM and CatBoost) using digital elevation model (DEM) error correction as a case study. The performance of the models is investigated across five landscapes in Cape Town South Africa: urban/industrial, agricultural, mountain, peninsula and grassland/shrubland. The models were trained using a selection of datasets (elevation, terrain parameters and land cover). The comparison entailed an analysis of the model execution times, regression error metrics, and level of improvement in the corrected DEMs. Generally, the optimised models performed considerably well and demonstrated excellent predictive capability. CatBoost emerged with the best results in the level of improvement recorded in the corrected DEMs, while LightGBM was the fastest of all models in the execution time for Bayesian optimisation and model training. These findings offer valuable insights for applying machine learning and hyperparameter tuning in remote sensing

Okonkwo’s Reincarnation: A Comparison of Achebe’s \u3cem\u3eThings Fall Apart\u3c/em\u3e and \u3cem\u3eNo Longer at Ease\u3c/em\u3e

Abstract: The reincarnation myth is a global concept, founded basically in religion and tradition. It was especially vibrant in the ancient times in places like Egypt, Greece, and in continents like Asia and Africa, which possess varying understandings of the myth. In Igbo tradition, for example, it is believed that reincarnation occurs within a family. And that some of the marks of reincarnation are usually the possession of the birthmark or certain other physical features and the exhibition of character and behavioral traits of a deceased person by a living member of his/her immediate or extended family. Thus, reincarnation entails the return to life of a deceased person in a new body. Sometimes, revenge is the mission of a reincarnated body. Bearing other reincarnation intentions in view, we study Achebe’s Okonkwo as one who falls within this category of reincarnation for revenge, having reincarnated through the body of his grandson Obi in No Longer at Ease to avenge himself against Umuofia and to suffer his son Nwoye, who now fathers him as Obi, for perhaps having had the effrontery to have left him and his ancestral tradition for the religion of the white man

Chemical Composition and Modeling of the Functions of Termitarium

The work was carried out in Fugro Consultants Nigeria Limited, Port Harcourt. It was the identification, extraction and characterization of chemical compounds responsible for making termination. The results showed that termitarium contains 11.6 (g/kg) of TOM; 15.9 (mg/kg) of Magnesium, 11690 (mg/kg) of iron and 23.3 (mg/kg) of extractable chloride which are responsible for structural stability of termitarium. The result also revealed that increase of clay content enhances the structural stability of termitarium. % water absorbed Vs time(s) for control and termitarium, mean number of seedings Vs time (days) for control and termitarium, gave their R-square (correlation coefficient) as 0.9975, 0.9951, 0.9959, 09807 and 0.09995 respectivel

TRANSFORMATIONAL LEADERSHIP AND ITS EFFECT ON EMPLOYEE PSYCHOLOGICAL WELL-BEING IN SELECTED DELTA STATE BROADCAST STATIONS

Main Objective: This study examined the effect of transformational leadership on the employee psychological well-being in selected Delta State broadcasting stations. Background Problems: Managers and governments across the globe are concerned about employee psychological health. Organizations in Nigeria have paid little attention to their employees' psychological well-being, and the majority of top executives in the broadcasting industry are unaware of the impact that transformational leadership has on the workforce's psychological well-being. The goal of this research is to investigate transformational leadership and its effect on employee psychological well-being in selected sample of Delta State broadcasting stations. Novelty: Transformational leadership is an effective type of leadership that encourages workers to perform better by increasing their levels of competence and self-reinforcement. Research Methods: The cross-sectional research design approach was used in this study. The participants in this study included 173 management and non-management employees from the Nigerian Television Authority in Asaba (47) and the Delta State Broadcasting Services in Asaba (69) and Warri (57) respectively. Using the SPSS software version 23.0, data was analyzed using linear regression. Findings/Results: According to the findings, transformational leadership had a significant and positive effect on employee psychological well-being. Conclusion: Transformational leaders work toward a common goal with followers, often self-sacrificing, prioritize employees, and develop them