Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management

Purpose – The present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS). Design/methodology/approach – The RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve. Findings – The very high (831–1200 km2) and high groundwater potential areas (521–680 km2) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC). Originality/value – Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans

Comparative Evaluation of Operational Land Imager sensor on board Landsat 8 and Landsat 9 for Land use Land Cover Mapping over a Heterogeneous Landscape

Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy (83.4%) than OLI (92.4%). The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran’s I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor

Remote Sensing and Artificial Intelligence for Urban Environmental Studies - Chapter 1

This chapter offers an outline of the concept of the book and its importance to scientists, researchers, stakeholders, and authorities. It seeks to provide a framework for the subjects that have been selected to address the knowledge gap that exists between urban studies and remote sensing with machine learning by fostering a deeper understanding of the scientific principles that underpin both subfields. This chapter also provides an overview of 28 articles written by the world's leading experts in the different fields of urban research, using earth observation databases, statistical techniques, and artificial intelligence to solve urban environmental issues

Rethinking Progress in Approaches and Techniques for the Urban Environmental Studies - Chapter 28

As a consequence of increasing urban population, the cities around the world had experienced very fast transformation of urban landscape which has resulted in serious consequences for the urban environment, landscape quality and climate.The researchers around the world have tried to model and analyse the urban expansion and its impacts on various aspects of urban environment. This chapter describes the various approaches and models used for analysing the urban expansion and its consequences for the urban environment in the last few decades. From mapping and modelling the urban spatial forms to achieving urban sustainability and resilience, the researches on urban studies have made significant progress in the past two centuries. In the past few decades, the urban researchers have examined the urban landscape pattern, urban sprawl, future urban expansion as well as the impacts of urban expansion on urban environment, ecosystem services, ecology and biodiversity, climatic system, etc. Moreover, the techniques used for analysing urban expansion and its consequences have also developed from the conventional field-based methods to the application of geospatial techniques and artificial intelligence (AI). The advent of AI along with innovative and advanced techniques has made it possible to deal with the negative consequences of urban expansion. Now the researchers are oriented towards achieving the urban resilience, promoting green buildings and increasing thermal comfort in the cities

Advancements in Urban Environmental Studies Application of Geospatial Technology and Artificial Intelligence in Urban Studies

According to UN estimates, approximately nearly half of the world's population now lives in cities and that figure is expected to rise to almost 70% by 2050. Cities now account for around 70% of worldwide greenhouse gas emissions, and this percentage is predicted to rise in the near future as a result of projected increases in global urbanization patterns. It is widely acknowledged that irrational urban planning and design can increase emissions while also exacerbating threats and risks, resulting in a slew of environmental issues such as urban heat islands, air pollution, flooding, amongst other issues, as well as environmental, social, and economic losses. Therefore, these concerns must be addressed promptly in order to cope up with these rising difficulties and make urban environments safer for residents. With the advancement of remote sensing technology and the use of current remote observation systems, urban data science, remote sensing, and artificial intelligence (AI), modeling and quantifying emergent difficulties in urban regions and urban systems have become easy. They aid in the quantitative analysis of urban shape, functions, and human behavior in cities. Harvesting data, developing models, and suggesting new methodologies will be aided by combining urban ecology with new breakthroughs in data science. This book is of great value to a diverse group of academicians, scientists, students, environmentalists, meteorologists, urban planners, remote sensing and GIS experts with a common interest in geospatial sciences within the earth environmental sciences, as well as human and social sciences

Long-term trends of groundwater level variations in response to local level land use land cover changes in Mumbai, India

Groundwater accounts for 50–80% of all domestic water use and 45–50% of all irrigation in India. With a projected 300 million people to be added to its urban population by 2050, cities will have a significant role on groundwater status in India. Therefore, this study examined the long-term trends in station level groundwater and the role of land use land cover change (LULCC) in Mumbai city. We utilized station level groundwater depth data for six stations, and LULC from satellite images from 1991 to 2018. The results revealed significant increase in groundwater depths across different seasons, particularly during the dry season. Steeper rates of decline were clustered around the northern interior of the city, which have experienced the maximum increase in urban impervious surfaces. Some of the stations located in the southern part of Mumbai experienced decreasing trends in groundwater depths during the wet season. This can be attributed to the low elevation and proximity to the seawaters resulting in saltwater intrusion. Groundwater exploitation from deeper aquifers and guided/engineered recharge to the top aquifers are the sustainable solutions. The results are particularly relevant for large urban agglomerations in the Global South, experiencing similar rates of rapid urbanization and population increase. [Display omitted

Identification and transcriptional profiling of UV-A-responsive genes in Bemisia tabaci

Ultraviolet-A (UV-A) radiation directly impacts the growth and spread of Bemisia tabaci. However, the mechanistic pathways of this phenomenon remain unknown. We analyzed B. tabaci transcriptome data after exposure to UV-A radiation for 6 h. The 453 genes were identified whose expression were significantly altered in response to the stress induced by UV-A irradiation. Forty genes were up-regulated, while 413 genes were down-regulated. Enrichment analysis using GO, KEGG, and Genomes databases revealed that the DEGs play key roles in antioxidation and detoxification, protein turnover, metabolic, developmental processes, and immunological response. Among the gene families involved in detoxification, shock, and development, down-regulated DEGs in transcriptional factor gene families were significantly greater than those up-regulated DEGs. Our findings demonstrated that exposure to UV-A stress can suppress immunity and affect the growth and biological parameters of B. tabaci by altering gene regulation. These results suggest a potential utility of UV-A stress in managing B. tabaci under greenhouse conditions

Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia

The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P) and REPTree (RF-REPTree), with three statistical models, i.e. weight of evidence, logistic regression and combination of WOE and LR. To predict the groundwater potential, nine groundwater potential conditioning parameters have been created. The Information Gain Ratio has been used to evaluate the impact of each parameter. The ROC curve has been used to validate the models. According to the findings, 15 to 30% of the study area has a very high or high groundwater potentiality. Furthermore, validation results revealed that RF based ensembles models outperformed other standalone models for groundwater potential modelling

Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms

The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwahati city using metaheuristic optimization algorithms integrated with random forest (RF) machine learning algorithm. Further, the receiver operating characteristic (ROC) and multiple error measurements were applied to analyze the performances of the models used. The result showed that about one-third of the area of Guwahati city is under the high and very high flood risk while nearly 50% area comes under low and very low flood risk. The value of the area under curve (AUC) of ROC was above 0.80 for all the integrated models applied. However, the RF-bee colony (BCO) and the RF-based ant colony (ACO) are the two best flood susceptibility models that performed better in the analysis. The methodology adopted in the study is cost and time effective and can be used for the flood susceptibility modeling in other parts of the world. Further, the findings of this study can useful in the flood mitigation and planning process