Department of Civil Engineering

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Author: search.filters.author.Srinivas, Rallapalli

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    Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques
    (Springer Nature, 2025-07) Srinivas, Rallapalli; Chalapathi, G.S.S.; Singh, Amit Rajnarayan
    Modeling the spatial variability and uncertainty of soil fertility parameters is crucial for sustainable agriculture but remains a challenge due to complex interactions between soil properties. Traditional models often assess individual parameters, such as pH or nitrogen (N), without considering their combined influence and uncertainty. This study develops a fuzzy logic and geoinformatics-based approach to simultaneously assess multiple soil fertility parameters. The model integrates 80 fuzzy rules to evaluate macro- and micronutrients, incorporating 250 soil samples analyzed using the PUSA Soil Test and Fertilizer Recommendation Meter (STFR). Experimental results showed soil fertility parameter ranges: pH (7.46–8.26), ECe (0.267–0.807 dS m−1), organic carbon (0.24–0.56%), N (85.56–146.32 kg ha−1), P (21.99–34.28 kg ha−1), K (116.41–156.16 kg ha−1), S (5.60–20.86 mg kg−1), Fe (1.065–5.095 mg kg−1), Mn (2.058–2.637 mg kg−1), Zn (0.748–1.105 mg kg−1), B (0.372–0.530 mg kg−1), and Cu (0.230–0.788 mg kg−1). The fuzzy model-derived fertility scores ranged from 41.55 to 52.60, with pH, organic carbon, nitrogen, phosphorus, potassium, and iron as critical parameters influencing fertility. Geostatistical kriging interpolation estimated fertility values at unsampled locations, generating a continuous, high-resolution soil fertility map for precision agriculture. Validation with crop yield data ranked suitability as: Pearl millet (0.919) > Mustard (0.890) > Wheat (0.863) > Barley (0.861). Multi-criteria decision analysis confirmed pearl millet as the most suitable crop based on fertility and yield potential. The study categorizes soil into low and moderate fertility zones across Jhunjhunu, Rajasthan, ensuring a systematic assessment for optimal nutrient management. By integrating fuzzy logic with GIS-based spatial modeling, this study enhances soil fertility classification, site-specific nutrient recommendations, and sustainable crop planning, reinforcing the role of fuzzy-GIS frameworks in precision agriculture.
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    Developing strategic and staging optimization pathways for urban flood damage mitigation
    (Elsevier, 2025-10) Srinivas, Rallapalli; Singh, Ajit Pratap; Goonetilleke, Ashantha
    Despite significant advancements in flood risk assessment and damage monetization, research is lacking for simultaneously examining the impacts of the complexity of factors such as rising sea levels, changing rainfall patterns, and urbanization, on flood damage assessment. This study adopts an innovative staging procedure that progressively and strategically optimizes flood damage mitigation measures while addressing the uncertainties associated with the implementation of flood mitigation measures over three different time horizons (2040, 2070, and 2100), with each subsequent stage refined based on the constraints and optimal results of the previous stage. Using the Non-dominated Sorting Genetic Algorithm (NSGA II), the study compares 27 optimized pathways for mitigating flood damages, balancing investment costs and Average Annual Damage (AAD) reduction. The results demonstrate that the proposed approach achieves an AAD reduction of up to 2.89% in 2040, 4.03% in 2070, and 2.12% in 2100 under the most comprehensive mitigation pathways while balancing the costs. The study highlights cost-effective alternatives, such as combining dredging and permeable asphalt, achieving a 1.31% AAD reduction in 2040 with no additional costs. Compared to static single-stage mitigation policies, the proposed staging approach offers greater flexibility and efficiency in addressing dynamic urbanization and climate change scenarios. These results underline the trade-offs between cost and effectiveness, equipping policymakers with a robust decision-making framework to tailor flood mitigation strategies for diverse global contexts. Overall, this study significantly advances the strategic planning of urban flood damage mitigation, enabling adaptation to evolving environmental and socio-economic challenges.
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    Bio-chelation for sustainable heavy metal remediation in municipal solid waste compost: a critical review of chelation technologies
    (Springer, 2025-04) Singhal, Anupam; Srinivas, Rallapalli
    Municipal solid waste (MSW) compost is a promising solution for sustainable urban waste management, widely used as a soil amendment and for carbon sequestration. However, heavy metals in MSW compost pose risks to ecosystems, food safety and human health. This review critically examines three decades of research (1994–2024) on heavy metal contamination in MSW compost and household hazardous waste (HHW), identifying gaps in managing these pollutants, particularly regarding hazardous waste co-disposal. It evaluates existing remediation strategies for heavy metal removal, with a focus on chemical-assisted leaching using chelating agents. Key treatment parameters—such as chelating agent concentration, pH, contact time, liquid/solid ratio, temperature and flow rate—are analysed in both batch and continuous modes. The study advocates for biodegradable chelating agents as an effective approach to enhancing MSW compost quality, with applications in landfill reclamation and agriculture. Emphasizing the need for eco-friendly heavy metal mitigation, the review underscores the importance of safe urban composting practices. The findings contribute to the circular economy and Sustainable Development Goals by promoting sustainable and safe MSW compost applications, fostering environmental protection and public health and guiding research and industry toward scalable, marketable remediation solutions.
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    Micro-macro–scale flood modeling in ungauged channels: Rain-on-grid approach for improving prediction accuracy with varied resolution datasets
    (Elsevier, 2025-06) Srinivas, Rallapalli; Munusamy, Selva Balaji; Gupta, Rajiv
    Flood risk arises from the interplay of climatic variability, urbanization, and mitigation measures. While climatic patterns exhibit variability that may either exacerbate or mitigate flood risk across regions, urban development continues to decrease the distance between human settlements and flood-prone areas, intensifying vulnerability. This also necessitates the utilization of datasets with diverse resolutions. Although several studies have performed flood forecasting using advanced models, challenges remain in addressing specific limitations such as (a) improving the accuracy of micro–macro-scale model transitions when employing varied resolution datasets, and (b) enhancing predictive capabilities for ungauged channels. This study aims to address these challenges within the context of a case study, applying a rain-on-grid approach to link micro- and macro-scale flood predictions in a data-scarce environment. The study investigated the impact of grid size and simulation time steps for daily rainfall data on computation time and model accuracy through Geo-HECRAS. The results highlighted significant impacts on the accuracy of hydrological simulations due to variations in spatial resolution and simulation time steps. Volume accumulation error decreased from 1.49 % to 0.25 % in micro-scale scenarios and from 0.85 % to 0.006 % in macro-scale scenarios when transitioning from higher-resolution grids (5 m and 30 m) to coarser grids (10 m and 50 m) with a finer simulation time step of 15 min. While finer grids improve spatial detail, the findings suggest that coarser grid resolutions, when combined with finer temporal scales, can achieve reduced errors and optimized computational efficiency for both micro and macro-scale modeling. This approach enhances the accurate representation of flood dynamics over broader spatial scales, ensuring the reliability of predictive models. It supports the development of flood mitigation strategies and resilient infrastructure tailored to both regional patterns and site-specific hydrological conditions.
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    Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices
    (Elsevier, 2024-06) Srinivas, Rallapalli
    Despite the availability of robust agricultural models, the successful on-ground implementation of all Best Management Practices (BMPs) remains elusive due to the lack of consideration for farmers' preferences. This study develops a unique network-based optimization technique coupled with Soil and Water Assessment Tool (SWAT) which delineates combined set of cost-effective BMPs at field-scale based on farmer's conservation identity in Ganges River Basin, India. SWAT was calibrated and validated using 10-year monthly discharge and nitrate data, and the selected BMPs were simulated using sequential uncertainty fitting framework. Farmers belonging to the Shahpur subdistrict were found to have the maximum normalized conservation identity of 90%, whereas the northeastern portion of the watershed contributed a maximum nitrate load of 7.5–9.92 kg/acre to the watershed outlet. An optimized combination of BMPs (riparian buffer-conservation tillage) with an efficiency of 0.28 was obtained through the modified network technique that targeted subbasins of the Morna subdistrict using farmer conservation identities. The results provide valuable guidance for watershed modelers, conservation planners, and researchers in pinpointing regions for targeted farmer training, subsidies, and other initiatives aimed at facilitating prompt field-scale BMP adoption. A flexible feature of the approach is its ability to modify the BMP action plans by replacing even technically feasible, cost-effective combinations based on farmers' preferences.
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    Agricultural watershed conservation and optimization using a participatory hydrological approach
    (Springer, 2024-07) Srinivas, Rallapalli
    Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders’ knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers’ opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.
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    Uncertainty-based fuzzified environmental-socio-economic risk assessment of precision agricultural practices
    (Springer, 2024-11) Srinivas, Rallapalli
    Precision agriculture (PA) is a management system that helps farmers address issues like fertilizer runoff, crop diseases, and declining yields by employing contemporary advanced technology. However, various environmental, socio-economic, and technological challenges hinder its widespread adoption. This study aims to bridge the gap between theoretical understanding of the PA techniques and their practical implementation. The present study proposes a fuzzy fault tree analysis (FFTA) based approach. The approach incorporates farmers’ willingness to adopt PA technology, presenting two novel aspects: (i) expert elicitation and fuzzy logic in fault tree analysis to estimate PA implementation failure probability, and (ii) assessment of farmer behavioral responses with uncertainties to identify critical adoption challenges. Results highlight the relative importance of challenges faced by different farmer categories. Key challenges include the high cost of technology (0.7604) and fertilizer dependence among innovators (0.7541), small landholdings among early adopters (0.7583), and resistance to new technology among late adopters (0.7637). Birnbaum’s measure effectively captured these challenges’ contributions to PA adoption failure. The proposed approach provides an ordered rank list of challenges, guiding stakeholders, researchers, and agricultural bodies to systematically address and mitigate these obstacles, enhancing PA implementation.
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    Efficacy of biochar as a catalyst for a Fenton-like reaction: Experimental, statistical and mathematical modeling analysis
    (Elsevier, 2025-02) Srinivas, Rallapalli; Goonetilleke, Ashantha
    This study employed machine learning (ML) techniques to identify the applicability of biochar as a catalyst for the Fenton-like process using PMS as an oxidant. Acetaminophen (ACT) was selected as the contaminant to perform the experimental study. Different biochars were used to catalyze peroxymonosulfate (PMS) for experimental data generation. The biochars were produced by post-pyrolysis thermal treatment at different detention times. Then, experiments using different materials, a single catalyst dose and PMS concentration were employed for ACT degradation. The 24 h heat-treated biochar (24BC) had the highest ACT degradation efficiency. Accordingly, different experimental conditions were investigated, including different doses and PMS concentrations. Further, the influence of ionic strength was investigated for the best ACT degradation conditions using different ions individually and combined. ACT degradation was found to be enhanced by the presence of ions. The analysis of chemical oxygen demand showed that despite complete ACT degradation being achieved, by-products generated remain in solution, suggesting incomplete mineralization. Finally, various statistical and ML models, including Random Forest, Linear Regression, KNN, Ridge, Lasso Regression, Support Vector Machine, Decision Trees, and Adaptive Neuro-Fuzzy Inference System were applied to predict and analyze the degradation efficiency of ACT using Biochar/PMS processes and to identify the ML technique most appropriate for the given experimental conditions. This study presents a preliminary investigation which aimed to assess the feasibility of machine learning techniques in analyzing biochar-mediated ACT degradation. While the findings are promising, further research with larger datasets is necessary to confirm and to generalize the conclusions.
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    Heavy metal remediation using chelator-enhanced washing of municipal solid waste compost based on spectroscopic characterization
    (Springer, 2023-04) Singhal, Anupam; Srinivas, Rallapalli
    Due to high metal toxicity, mixed municipal solid waste (MSW) compost is difficult to use. This study detected the presence of heavy metals (Cd, Cu, Pb, Ni, and Zn) in MSW compost through mineralogical analysis using X-ray diffraction (XRD) and performed topographical imaging and elemental mapping using a scanning electron microscope and energy dispersive X-ray analysis (SEM–EDX). Ethylenediaminetetraacetic acid (EDTA), a typical chelator, is tested to remove heavy metals from Indian MSW compost (New Delhi and Mumbai). It deals with two novel aspects, viz., (i) investigating the influence of EDTA-washing conditions, molarity, dosage, MSW compost-sample size, speed, and contact time, on their metal removal efficiencies, and (ii) maximizing the percentage removal of heavy metals by determining the optimal process control process parameters. These parameters were optimized in a batch reactor utilizing Taguchi orthogonal (L25) array. The optimization showed that the removal efficiencies were 96.71%, 47.37%, and 49.94% for Cd, Pb, and Zn in Delhi samples, whereas 45.55%, 79.52%, 59.63%, 82.31%, and 88.40% for Cd, Cu, Pb, Ni, and Zn in Mumbai samples. Results indicate that the removal efficiency of heavy metals was greatly influenced by EDTA-molarity. Fourier-transform infrared spectroscopy (FTIR) confirmed the presence of hydroxyl group, which aids heavy metal chelation. The results reveal the possibility of EDTA to reduce the hazardous properties of MSW compost.
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    Effect of urbanization on the urban lake water quality by using water quality index (WQI)
    (Elsevier, 2023-07) Srinivas, Rallapalli; Singhal, Anupam
    Lake water serves an efficient source of drinking, irrigation, agriculture, industry, construction, domestic and recreation use for the urban and rural population of developing countries. The paper focuses on the assessment of water quality on the selected lakes which is affected by the speedy development of the city under the sprawl of urbanization and concretization by applying Water Quality Index (WQI) tool. Four lakes, namely Hebbal, Ulsoor, Allasandra and Mahadevapura are selected in the silicon city, Bengaluru for water quality assessment. A total of 10 parameters were taken into consideration, such as pH, turbidity, total alkalinity, total acidity, total phosphorus, COD, BOD, DO, nitrates and total nitrogen from 2 sampling sites depending upon the source of wastewater or sewage discharges. Water samples were collected and prepared for composite samples. These composite samples were examined for their different chemical and physical properties and the results were compared with standard permissible values. The results of WQI of Hebbal Lake (70.89–72.74), Ulsoor Lake (83.44–83.3), Allasandra Lake (54.47–51.84) and Mahadevapura Lake (159.41–155.81) showed that the lakes fall under poor, very poor and unsuitable categories. The results pointed out the anthropogenic activities and entry of untreated sewage into the lake. This confirms the urgent need for regular monitoring of lakes and setting up of certain policies for lake water management.