Practical techniques in prioritizing flood and erosion hazards potential zones in watersheds

Globally, soil loss is one of the most serious environmental threats in recent time. Soil erosion by water has been acknowledged as a serious concern due to its impacts on the environment and the society at large. The severity of soil erosion is a product of various factors such as topography, soil features, severity of rainfall, runoff, cultivation mechanism and land cover. Erosion is mainly caused by anthropogenic activities aided by geomorphologic processes. Soil loss affects natural resources and agricultural production through the removal of fertile top soil with significant impacts on crop production and quality of the ecosystems. It has also been reported to cause dam and reservoir sedimentation, land wastage, and economic loss. Flood is an abnormal situation of water flow outside the original borders of a defined water body. Floods results from complex hydrological, geological and geomorphologic conditions with substantial social, economic and environmental damages. The occurrence of flood worldwide over the last forty (40) years has increased. About 31% of economic losses from natural disasters are caused by floods making it the most expensive natural hazards globally.

The influence of spatiotemporal dynamics in land use/land covers on flood hazard

Flood is an abnormal situation of water flow outside the original borders of a defined water body. Floods results from complex hydrological, geological and eomorphologic conditions with substantial social, economic and environmental damages. Owing to the immense destruction of properties, infrastructure and loss of lives, floods are one of the most destructive natural hazards. The estimated global economic damage associated with floods since the end of the 20th century was 386 billion USD. The objective of this work is to define flood risk in different studied zones based on the use of remote sensing, GIS and artificial intelligence techniques. One of the most important factors to understand and define the flood risk phenomena is to study the change of spatiotemporal dynamics of land use/land cover and its impact on flood susceptible areas during decades. For this, the proposed subject will evaluate the influence of land use /land cover changes on mapping flood areas using remote sensing, GIS and Artificial intelligence techniques.

Improving the assessment of surface water quality using artificial intelligence techniques

Surface water quality assessment plays a critical role in environmental monitoring and management efforts. This study focuses on evaluating surface water quality using various indicators and employing machine learning techniques to model these indicators. By leveraging multiple indicators such as physical, chemical, and biological parameters, comprehensive assessments of water quality can be obtained. Machine learning algorithms are then applied to model the relationships between these indicators and water quality, enabling accurate predictions and spatial-temporal analysis. The integration of diverse indicators and machine learning techniques offers a holistic approach to surface water quality evaluation, providing valuable insights for effective decision-making and sustainable water resource management. The present project underscores the significance of utilizing diverse indicators and machine learning methods in surface water quality assessment, highlighting their potential for enhancing environmental monitoring and management practices.

Surface water quality evaluation using GIS, remote sensing and artificial intelligence techniques

Surface water quality assessment is crucial for effective water resource management and environmental conservation. In recent years, the integration of remote sensing technology and artificial intelligence techniques has emerged as a promising approach for evaluating surface water quality. This interdisciplinary approach leverages the capabilities of remote sensing to provide spatially and temporally comprehensive data on water quality parameters, while machine learning algorithms offer efficient and accurate analysis of these data. By combining remote sensing and artificial intelligence, researchers can enhance the monitoring, assessment, and management of surface water quality, leading to informed decision-making and sustainable water resource utilization. This work highlights the importance of integrating remote sensing and artificial intelligence in surface water quality evaluation, emphasizing its potential for addressing contemporary water management challenges.