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Remote Sensing And Image Interpretation

Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capacity of the applied method is quite good. Finally, the spatial distribution of the urban areas and the road network were compared with the derived suitability map and the results revealed that approximately 50% of both of them are located within areas susceptible to natural hazards. The proposed approach can be useful for engineers, planners and local authorities in spatial planning and natural hazard management.

Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study

Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms

Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.

Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach

This study investigated the trends and variability of seasonal and annual rainfall and temperature data over southern Ethiopia using time series analysis for the period 1983–2016. Standard Anomaly Index (SAI), Coefficient of Variation (CV), Precipitations Concentration Index (PCI), and Standard Precipitation Index (SPI) were used to examine rainfall variability and develop drought indices over southern Ethiopia. Temporal changes of rainfall trends over the study period were detected using Mann Kendall (MK) trend test and Sen’s slope estimator. The results showed that the region experienced considerable rainfall variability and change that resulted in extended periods of drought and flood events within the study period. Results from SAI and SPI indicated an inter-annual rainfall variability with the proportions of years with below and above normal rainfall being estimated at 56% and 44% respectively. Results from the Mann Kendall trend test indicated an increasing trend of annual rainfall, Kiremt (summer) and Bega (dry) seasons whereas the Belg (spring) season rainfall showed a significant decreasing trend (p < 0.05). The annual rate of change for mean, maximum and minimum temperatures was found to be 0.042 °C, 0.027 °C, and 0.056 °C respectively. The findings from this study can be used by decision-makers in taking appropriate measures and interventions to avert the risks posed by changes in rainfall and temperature variability including extremes in order to enhance community adaptation and mitigation strategies in southern Ethiopia.

/pdf/analysis-of-climate-variability-and-trends-in-southern-3ctdz65o8z.pdf

Analysis of Climate Variability and Trends in Southern Ethiopia

Analysis that is based on geostatistics eliminates many corresponding defects and limitations compared to classical statistics which have been developed by random distribution theory of processes and variables. Interpolation is important for local analysis by GIS, because many maps used for GIS operation are made by interpolation. In this research, two different methods which is Kriging method and Inverse Distance Weighted (IDW) method was examined for developing Digital Elevation Model image. Each method’s advantages and disadvantages were considered. The study are, Kepil, is within Kulon Progo physiographic and stratigraphic area, located in the western part of Yogyakarta city. This area is located close to the Java Island Subduction Zone, hence influence of tectonic plate movement is relatively dominant. Geological structures become a main factor that shapes the recent morphology. This study area also has many settlements and has high weathering and erosion rate. Lineaments are extracted based on Digital Elevation Model to provide assistance in delineating geological structures. The structural geology analysis and an understanding of tectonic phase of the area provide useful information for geological map- ping. Accuracy of lineament depends on extraction and imagery parameters used. In this study, the extraction was conducted by two different raster methods, namely Kriging and Inverse Distance Weighted (IDW) with the same resolution of 30 meters. Lineament extracted automatically (digitally) with certain parameter settings. Keywords: Kriging, inverse distance weighted, interpolation, lineament, random distribution, digital elevation model.

https://jurnal.ugm.ac.id/jag/article/download/7204/5643

Comparison of kriging and inverse distance weighted (idw) interpolation methods in lineament extraction and analysis

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Comparison of kriging and inverse distance weighted (idw) interpolation methods in lineament extraction and analysis