Assessing impact of urbanisation on surface runoff using vegetation-impervious surface-soil (V-I-S) fraction and NRCS curve number (CN) model

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

“…where LQCI represents the Landscape Quality Composite Index, 𝑤 𝑗 is the indicator j criterion weight, 𝑉 𝑖𝑗 is the degree of normalization of the jth criterion in year i, and n is the number of indicators (Bera et al, 2022;.…”

“…Water bodies and vegetation landscapes are critical components of urban blue-green systems and play essential roles in assessing urban ecological and environmental quality (Macsimovici, 2019;Mu et al,2020). They also serve as sources of information that shape users' perceptions of landscape quality and act as natural media and material carriers to maintain a harmonious relationship between humans and nature (Bera et al, 2022). The scientific and rational planning of blue-green landscapes is crucial for assessing the quality of urban environmental landscapes (Cheng & Wu,2020;Li & Lange,2023).…”

“…The assessment of ecological service quality in landscape systems is a complex and multifaceted undertaking that encompasses resource, social, and economic objectives, levels, and factors (Ke Fu, 1992). Various methods have been applied for the comprehensive evaluation of multiple indicators, such as factor analysis (Bera et al, 2022), the Delphi method (Zheng et al, 2017), and hierarchical analysis . However, these mathematical models possess certain shortcomings and lack a theoretical basis for weight determination, rendering them impractical for real-world applications.…”

“…Yang et al (2020) employed the BP optimization algorithm to measure waterfront and plant landscape structures on a small scale, and their results demonstrated that the algorithm's convergence speed and warp were stronger than those of other algorithms, leading to a more accurate assessment of landscape quality levels (Yang, 2020). Finally, Bera et al (2022) used a GA-BP neural network to develop an evaluation model for urban green development, which successfully trained and tested 20 valid indicators, such as sewage treatment, green coverage, forest coverage, urbanization level, and population size, resulting in data clustering analysis and prediction results consistent with objective reality. In conclusion, GA-BP neural networks are powerful tools for assessing landscape quality and evaluating ecological service quality in landscape systems, providing accurate and efficient solutions to practical problems (Pu & Cai, 2022).…”

Scientific foundations in research in Engineering

“…Для міст, що є осередками антропогенної діяльності, ситуація може бути ускладнена комплексним впливом низки негативних чинників, таких як автотранспорт[291], промисловість[292,293], поверхневий стік[294], побутові відходи[295,296] тощо. Ці фактори часто чинять сумарний ефект на елементи довкілля та вказують на нагальну потребу інноваційних підходів до збереження природної складової міст[297,298].По-друге, серед іншого можна відзначити додатковий вплив медичних відходів, що утворились при пандемії COVID -19, на стан захаращеності міст та потребу в логістичній організації поводження з цією групою відходів `[ 299,300].…”