Rapid change of Adama wereda during the last three decades has posed a serious threat to the existence of ecological systems, specifically water bodies which play a crucial part in supporting life. Role of Satellite images in Remote Sensing could be more important in investigation, monitoring dynamically and planning of natural surface water resources. Landsat-5(TM) & Landsat 8 (OLI) has high spatial, temporal and multispectral resolution and therefore provides consistent and perfect data to detect changes in surface changes of water bodies. In this paper, a study was conducted to detect the changes in water body extent during the period of 1984, 2000 and 2017 using various water indices such as namely Water Ratio Index (WRI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), supervised classification and wetness component of K-T transformation and the results are Presented. NDWI has been adopted for this study as compared with other indices through ground survey. The results showed an intense decreasing trend in the lakes of chelekleka, kiroftu, lake 1 and lake 3 of surface area in the period 1984–2017, especially between 2000 and 2017 when the lake lost about 1.309 km² (one third) of its surface area compared to the year 2000, which is equivalent to 76%, 18%, 0.03% and 96%. Interestingly koka lake has shown very erratic changes in its area coverage by losing almost 3.5 km² between 1984 and 2000 and then climbing back up by 14.8 km² in 2017. Percentage of increment was observed that 10.6% as compared with previous year.

Two very important major factors that have driven rapid growth of cities and towns are population growth and large-scale migration. Urban buildup information is required for enormous application for planning land use and management. A challenging task is to extract urban build up areas from moderate-resolution Landsat images due to intra-urban heterogeneity and spectral confusion between other land cover types. In this study, urban area was extracted from Landsat series of thematic mapper (TM) and object land imager (OLI) between 1984 and 2017 of Adama city in Ethiopia. Study chose two indices, Normalized Difference in Built-up Index (NDBI) and Normalized Difference in Vegetation Index (NDVI) to represent three major urban land use classes, i.e., built-up/barren/bare land, open waterbodies, and vegetation. Built-up area has been extracted by taking difference between NDBI and NDVI to remove water and vegetation noises and the resulting image was spectrally segmented to separate built-up areas. Derived index was utilized to plot decade difference of built-up area from 1984 to 2017. In order to compute Shannon entropy value, the study area was divided into 52 equal zones to quantify urban sprawl. The expansion of the built-up area has been revealed as a major change in the area when city area expanded substantially by six times 51.3364 (sq. km) area of 1984 (8.80 sq. km).

In the rapidly evolving landscape of urbanization, cities face unprecedented challenges due to climate change. As frontline battlegrounds against global warming, urban areas increasingly rely on AI to bolster resilience and sustainability. This chapter explores AI's role in shaping urban resilience, drawing from research and real-world applications. AI revolutionizes climate modeling, using vast datasets and machine learning to create predictive models that anticipate and mitigate impacts like extreme weather and sea-level rise, enabling proactive urban planning.AI also enhances smart infrastructure and resource management, optimizing energy use, water resources, and transportation to minimize ecological footprints. Through real-time monitoring and adaptive systems, cities can respond to environmental changes, ensuring infrastructure resilience. AI democratizes decision-making by empowering citizen participation in climate efforts. Open data, cross-sector partnerships, and collaborative research drive innovation, accelerating the transition to climate-resilient urban futures.

Accurate retrieval of Land Surface Temperature (LST) is critical for assessing the cooling effects of urban green spaces (UGS) and blue spaces (UBS), which help mitigate the urban heat island effect and improve thermal comfort. This study introduces a novel methodology that integrates deep learning with domain expertise to predict LST, using real LST data, vegetation spectral indices, and spectral bands as inputs. A 1-Dimensional Convolutional Neural Network (1D-CNN) was developed, which outperformed conventional machine learning and alternative deep learning models, demonstrating high predictive accuracy and strong generalization ability. Spatial regression analyses were further employed to examine how UGS of varying sizes influence LST. Results revealed that larger UGS provide strong cooling benefits, while smaller patches contribute less. Remote sensing data from 1991 to 2022 confirmed the significant role of both green and blue spaces in mitigating urban heat, with notable cooling observed around wetlands, rivers, and urban parks. Importantly, combined green–blue configurations enhanced cooling more effectively than blue spaces alone, indicating synergistic benefits when vegetation is integrated with water bodies. Case analysis of afforestation in Daan Forest Park demonstrated how urban greening initiatives can substantially lower local temperatures. Similarly, UBS surrounded by adjacent vegetation exhibited greater temperature reductions compared to isolated water bodies. These findings underscore the need to preserve and expand large, continuous green areas while enhancing connections between green and blue infrastructures. The proposed framework provides robust evidence and practical guidance for urban planners and policymakers to design climate-resilient cities that maximize the co-benefits of UGS and UBS.