About

Srinivas Rallapalli is an Adjunct Assistant Professor in the Department of Bioproducts and Biosystems Engineering at the University of Minnesota Twin Cities. His areas of interest include environmental and ecological engineering. His research contributions encompass a range of topics related to water resource management, agricultural watershed conservation, and sustainable agricultural practices. He has been involved in developing advanced hydrological modeling techniques, hydro-economic models, and decision support frameworks aimed at improving water quality and resource efficiency in agricultural landscapes. His work often integrates innovative approaches such as LiDAR-based modeling, bio-inspired algorithms, and artificial intelligence to address complex environmental challenges.

Research topics

• Computer Science
• Environmental science
• Geography
• Environmental resource management
• Business
• Computer Security
• Ecology
• Engineering
• Artificial Intelligence
• Environmental economics
• Geology
• Economics
• Civil engineering
• Remote sensing
• Agricultural engineering
• Water resource management
• Cartography

Selected publications

• Adaptive Basin Management under Determinate and Indeterminate Sustainability Factors

  Environmental Management · 2026-01-30

  articleSenior authorCorresponding
  PublisherDOI

• Agro-AI Decision Support System: Hydro-Economic and Farmer Centric Smart Crop Diversification

  2026-01-01

  articleOpen accessSenior author
  PublisherDOI

• Optimizing the upcycling of microplastics to a carbon-based adsorbent for water treatment: An integrated experimental and computational approach

  Chemical Engineering Science · 2026-01-28

  article
  PublisherDOI

• Targeting degraded hotspots in riparian corridors for rehabilitation based on hydrological, ecological, natural and anthropogenic indicators

  Journal of Environmental Management · 2026-01-01

  articleCorresponding
  PublisherDOI

• Efficacy of biochar as a catalyst for a Fenton-like reaction: Experimental, statistical and mathematical modeling analysis

  Journal of Water Process Engineering · 2025-01-18 · 3 citations

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  PublisherDOI

• Developing strategic and staging optimization pathways for urban flood damage mitigation

  Journal of Hydrology · 2025-04-15 · 1 citations

  articleCorresponding
  PublisherDOI

• Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques

  Scientific Reports · 2025-07-23 · 9 citations

  articleOpen access

  Abstract 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.

  PublisherOA PDFDOI

• Bentonite swelling behavior under physicochemical controls: A critical review on thermal–electrolytic conditions and mineralogical determinants for nuclear waste management

  Journal of environmental chemical engineering · 2025-11-16

  articleCorresponding
  PublisherDOI

• Optimizing water-efficient agriculture: evaluating the sustainability of soil management and irrigation synergies using fuzzy extent analysis

  Scientific Reports · 2025-08-11 · 7 citations

  articleOpen access

  Sustainable agriculture demands the integration of optimized irrigation and soil tillage practices. Poor selection or mismatched combinations of these practices can lead to inefficient resource use, declining soil health, and reduced crop productivity. Despite extensive research on individual tillage and irrigation methods, limited studies explored their combined effects on multiple agricultural sustainability parameters. This gap underscores the need for a comprehensive assessment framework that can guide farmers and stakeholders in identifying optimal combinations for diverse agricultural objectives. This study employs a compounded fuzzy extent analysis to evaluate the cumulative impact of various soil tillage and irrigation methods on key agricultural parameters, including affordability, maximum yield, climate resilience, water usage, soil disruption, disease resistance, ease of operation, nutrient utilization, and crop diversification. The analysis compares individual practices and their combinations using comparative matrices to identify the most suitable options across all parameters. The fuzzy logic approach addresses data uncertainty by converting linguistic variables into triangular fuzzy numbers, enabling more accurate decision-making. The results indicate that Zero Tillage is the most effective tillage practice (score of 0.176), while Deficit Irrigation emerges as the most efficient irrigation method, scoring 0.144. The research suggests that integrating Zero-Tillage (ZT) with Deficit Irrigation (DI) is the most cost-effective agricultural practice. Additionally, combining No-Tillage (NT) with Surface Irrigation and Mulching (SIM) results in higher yields and improved water use efficiency. Furthermore, the synergy of No-Tillage (NT) and Drip Irrigation (DI) enhances crop resilience to climate change. These findings provide valuable insights for developing sustainable agricultural strategies that balance productivity, resource conservation, and environmental protection.

  PublisherDOI

• Compounded fuzzy entropy-based derivation of uncertain critical factors causing corrosion in buried concrete sewer pipeline

  npj Clean Water · 2025-05-06 · 3 citations

  articleOpen access

  Abstract Corrosion in buried concrete sewer pipelines remains a critical challenge for infrastructure sustainability, driven by the complex interplay of environmental, material, operational, pipe-related, and physical factors with inherent uncertainty and interdependency, aspects often overlooked previously. This study introduces a novel compounded fuzzy entropy-based approach to systematically prioritize critical corrosion-inducing factors, integrating environmental (H₂S, pH, humidity, temperature, O₂), material (cement content, alkalinity, w/c ratio, porosity, permeability), pipe-related (age, length, diameter, depth, slope), operational (flow velocity, water pressure, hydraulic energy loss, sewage residence time, sewer type), and physical (soil type, corrosivity, moisture, groundwater level, external load) factors. Results identify H₂S (0.2073), pH (0.2055), humidity (0.2031), pipe age (0.2039), length (0.2019), cement content (0.2026), alkalinity (0.2015), water pressure (0.2073), flow velocity (0.2043), soil type (0.2042), and soil corrosivity (0.2025) as the most influential contributors, enabling targeted corrosion mitigation strategies and enhancing infrastructure resilience.

  PublisherOA PDFDOI

Frequent coauthors

• Ajit Pratap Singh

  28 shared

• Chaitanya Kapoor

  27 shared

• Aadith Warrier

  Birla Institute of Technology and Science, Pilani

  27 shared

• Pratik Narang

  Birla Institute of Technology and Science, Pilani

  27 shared

• Mohit Singh

  27 shared

• Chan Hsu

  National Cheng Kung University

  25 shared

• Harish Pupalla

  National Sun Yat-sen University

  25 shared

• Navneeth P Sagar

  SRM University

  25 shared