eChapters

Electronic health records contain extensive clinical, demographic, diagnostic and treatment information that can support medical research and decision-making. This chapter examines the characteristics of healthcare big data and the challenges associated with EHR storage, integration, quality and analysis. It presents an analytics pipeline covering data ingestion, secure storage, preprocessing, integration, pattern discovery, visualization and clinical decision support. Machine learning and deep learning methods are discussed in relation to clinical phenotyping, risk stratification, predictive epidemiology and adverse-outcome prediction. The chapter also addresses data privacy, governance, regulatory compliance, explainability, scalability and bias, highlighting the need for secure and interoperable EHR-mining systems that generate clinically useful knowledge.

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Data-centric artificial intelligence has transformed medical imaging, clinical prediction and electronic health-record analysis, but purely data-driven models face limitations involving generalization, bias, privacy and interpretability. This chapter examines the transition toward knowledge-aware and hybrid healthcare systems that combine machine learning with clinical knowledge, ontologies and knowledge graphs. It discusses explainable AI, clinical reasoning and the integration of structured knowledge into intelligent decision support. Applications include acute-care support, precision medicine, genomic oncology and population-health management. Ethical issues, regulatory compliance, fairness and privacy-preserving technologies are also considered. Future directions include quantum computing, large language models, multimodal integration and equity-focused healthcare-system development.

Healthcare organizations require collaborative learning methods that protect sensitive patient information. This chapter introduces federated learning as a framework for training models across decentralized institutions without transferring raw clinical data. Horizontal, vertical and hybrid federated architectures are examined alongside communication protocols, threat models and security requirements. Privacy-enhancing methods—including differential privacy, secure aggregation, cryptographic techniques and trusted execution environments—are discussed. Applications in medical imaging, electronic health-record analysis, wearable devices and remote patient monitoring demonstrate the framework’s practical value. The chapter also evaluates accuracy, privacy, communication cost, scalability and robustness while addressing data heterogeneity, fairness, explainability, legal compliance and ethical challenges in collaborative healthcare analytics.

Intelligent healthcare is advancing through the integration of multimodal data, trustworthy AI, federated learning, digital twins, IoT devices and real-time decision-support systems. This chapter reviews the evolution of healthcare-data analysis and examines emerging methods for intelligent diagnosis and predictive care. It discusses explainability, privacy-preserving analytics, interoperability, data governance and ethical frameworks necessary for responsible implementation. Wearable technologies and connected sensors support continuous monitoring, while human–AI collaboration improves clinical decision-making. Case studies illustrate practical healthcare applications and implementation strategies. The chapter also considers global-health requirements, particularly in developing countries, and identifies future opportunities for scalable, equitable, secure and clinically effective intelligent healthcare systems.

Computational intelligence is central to the development of proactive, personalized and connected healthcare systems. This chapter examines artificial intelligence, machine learning, expert systems, medical-image analysis, IoT, big-data analytics, cloud computing and blockchain as key enabling technologies. It presents intelligent architectures for diagnosis, monitoring, treatment support and secure health-data management. Privacy, cybersecurity, explainability, bias, fairness, scalability and deployment challenges are discussed in relation to practical healthcare implementation. The chapter proposes a research roadmap involving trustworthy AI, advanced privacy-preserving mechanisms, edge-cloud intelligence, digital twins, multi-omics integration and personalized health models. It also emphasizes accessible computational-intelligence solutions for rural and resource-constrained healthcare environments

A flood occurs when water exceeds the carrying capacity of a river, stream, drain, gully, or another natural or artificial channel and spreads over adjoining land. Its severity depends on water volume, depth, velocity, duration, timing, and the vulnerability of affected communities. Prolonged inundation can severely damage crops, property, infrastructure, water supplies, and human and animal life. Night-time floods may be particularly dangerous because warnings and evacuation become difficult. This chapter introduces flood occurrence and mechanisms, India’s and the world’s flood history, flood-prone regions, flood types and causes, major consequences, and the responsibilities of Indian flood-control and forecasting agencies.

Hydrology examines the occurrence, circulation, distribution, and properties of water in the atmosphere and on Earth. Solar energy drives the hydrologic cycle through evaporation, condensation, precipitation, infiltration, runoff, and groundwater movement. Rainfall first satisfies interception, depression storage, evaporation, and infiltration before excess water begins flowing across the surface and into channels. When runoff entering a river exceeds its conveyance capacity, flooding may occur. This chapter explains clouds, precipitation measurement, rain-gauge networks, rainfall consistency, infiltration, runoff mechanisms, hydrographs, unit hydrographs, empirical runoff models, and stage-discharge relationships. These concepts provide the scientific foundation required for analysing floods and designing appropriate control measures.

Rainfall, runoff, evaporation, and other hydrological variables exhibit considerable natural uncertainty and therefore cannot be treated as entirely deterministic phenomena. Statistical and probability methods make it possible to organize observations, describe their variability, identify relationships, estimate extreme events, and predict values associated with selected probabilities. Hydrological data may be analysed daily, weekly, monthly, seasonally, or annually and expressed through averages, maxima, minima, dispersion, skewness, and frequency. This chapter introduces statistical terminology, correlation, regression, probability distributions, plotting positions, goodness-of-fit tests, model-evaluation tools, reliability, standard error, and risk analysis. These techniques are essential for flood-frequency analysis, planning, and hydraulic design.

Flood estimation determines the probable magnitude and frequency of flood events for designing hydraulic structures and managing flood risk. Intense rainfall over a catchment generates excess water after interception, infiltration, evaporation, and depression storage have been satisfied. This runoff reaches the drainage system and may cause flooding when channel capacity is inadequate. Reliable estimates help engineers select suitable dimensions for dams, spillways, bridges, culverts, reservoirs, and other protective works. This chapter describes rainfall-based and streamflow-based estimation techniques, including the Rational and Cook’s methods, unit-hydrograph procedures, frequency distributions, Gumbel, log-normal and Log-Pearson Type III analyses, empirical formulae, flow-mass curves, and partial-duration series.

Flooding and soil erosion are closely connected because concentrated runoff detaches, transports, and deposits soil material throughout a watershed and river system. Raindrop impact initiates splash erosion, while surface flow produces sheet, rill, and eventually gully erosion. Sediment generated from fields and channels reduces reservoir capacity, raises riverbeds, obstructs drainage, and lowers channel conveyance, thereby increasing flood risk. This chapter explains erosion mechanisms, classifications, influencing factors, gully development, sediment yield, sediment-delivery ratio, suspended and bed loads, and sediment-transport models. It also examines soil-loss estimation and agronomic or structural conservation measures that can reduce erosion, control sediment movement, and improve watershed-level flood management

Gullies convey runoff and sediment from upstream catchments, but uncontrolled flow enlarges their beds and banks, removes productive land, and may ultimately create ravines. Their development depends on rainfall intensity, runoff volume, catchment size, land use, soil properties, topography, and channel characteristics. Effective treatment requires reducing runoff generation, diverting excess water where appropriate, stabilizing the gully, and restoring vegetation. This chapter presents the principles of gully control and classifies interventions into upstream and downstream measures. It discusses agronomic practices, diversion structures, check dams, drop structures, chute spillways, bank protection, revegetation, and maintenance requirements for safely controlling water velocity, erosion, and sediment transport.

An earthen dam is an embankment constructed primarily from compacted soil to impound runoff and create a reservoir. Such dams support irrigation, water supply, groundwater recharge, hydropower generation, and flood moderation. Their successful performance depends on suitable site conditions, proper materials, adequate compaction, controlled seepage, stable slopes, and safe disposal of surplus water. This chapter introduces the advantages and limitations of earthen dams, construction materials, types of embankment sections, and their principal components. Particular attention is given to cut-offs, impervious cores, casing or shells, foundations, filters, drainage systems, spillways, outlets, slope protection, and common causes of deterioration or structural failure.

Designing an earthen dam requires the coordinated evaluation of catchment runoff, reservoir capacity, site conditions, foundation properties, expected service life, and the consequences of failure. A selected return period is used to estimate extreme rainfall and runoff, while adequate spillway capacity and freeboard protect the embankment against overtopping. The dam must possess low permeability, stable upstream and downstream slopes, and sufficient resistance to seepage, settlement, erosion, and earthquake forces. This chapter presents design criteria, top width, dam height, side slopes, freeboard, seepage-control components, slope-protection measures, and stability considerations. It also explains procedures needed to create a safe, economical, and functional embankment.

Seepage is the movement of water through the soil body and foundation of a hydraulic structure. In an earthen dam, uncontrolled seepage may produce excessive water loss, high pore-water pressure, slope instability, internal erosion, piping, or complete structural failure. Seepage analysis therefore determines the quantity and path of water movement and helps engineers design filters, drains, impervious cores, cut-offs, and other protective components. This chapter explains the objectives and importance of seepage analysis, Darcy’s law, permeability, seepage velocity, phreatic lines, flow nets, equipotential lines, and flow channels. It also examines seepage forces, piping conditions, exit gradients, and methods for improving dam safety.

A reservoir stores water behind a dam and can reduce downstream flooding by temporarily retaining part of the incoming flood volume. Its effectiveness depends on available storage, inflow characteristics, outlet capacity, operating rules, forecasting information, and the timing of releases. Reservoirs may also serve irrigation, municipal supply, hydropower, navigation, recreation, and environmental purposes, making their planning a multipurpose undertaking. This chapter introduces reservoir terminology, classifications, storage zones, capacity determination, site selection, sedimentation, and operating principles. It explains how flood-storage space modifies peak discharge and discusses reservoir routing, release regulation, design limitations, safety considerations, and the role of coordinated reservoir systems in basin-wide flood management.

A levee is an elongated embankment constructed along a river, stream, or floodplain boundary to prevent floodwater from entering protected land. Levees can safeguard settlements, agricultural fields, industries, roads, and public infrastructure, but they do not eliminate flooding and may transfer risk downstream or cause severe damage if overtopped or breached. Their design depends on expected flood level, freeboard, soil properties, seepage conditions, foundation stability, alignment, and available construction materials. This chapter explains levee functions, classifications, advantages, limitations, and structural components. It also addresses seepage control, slope protection, erosion, settlement, failure mechanisms, inspection, maintenance, and the importance of combining levees with forecasting and evacuation planning.

A detention reservoir temporarily stores storm runoff and releases it gradually at a controlled rate, thereby reducing the peak discharge reaching downstream channels and communities. Unlike a conventional conservation reservoir, its principal function is short-term flood moderation rather than permanent water storage. Its performance depends on basin capacity, inlet and outlet arrangements, catchment response, design storm, allowable downstream discharge, and available drawdown time. This chapter explains the concept, purposes, and functioning of detention reservoirs and distinguishes them from ordinary storage reservoirs. It covers their classifications, site requirements, storage estimation, outlet design, routing principles, benefits, limitations, maintenance, and application in rural watersheds, urban drainage systems, and integrated flood-control programmes.

Channel improvement increases or restores the ability of a river or drainage channel to convey floodwater safely. Natural or artificial channels may lose capacity because of sediment deposition, vegetation, debris, bank encroachment, irregular alignment, insufficient cross-sectional area, or excessive hydraulic resistance. Improvement can involve widening, deepening, straightening, clearing, lining, dredging, bank stabilization, or constructing additional waterways. This chapter discusses the purposes, advantages, disadvantages, and planning principles of channel improvement. It examines channel geometry, flow resistance, permissible velocity, sediment behaviour, erosion protection, and maintenance. The chapter also emphasizes that interventions must be evaluated carefully because poorly planned modifications can accelerate flow, destabilize banks, damage habitats, or shift flooding downstream.

River-training works guide and regulate the flow of a river to achieve a stable channel alignment, protect banks, improve navigation, prevent erosion, and reduce flood damage. Rivers naturally change their course through sediment deposition, bank cutting, meandering, and variations in discharge. Engineering interventions must therefore account for hydraulic forces, sediment movement, channel morphology, and long-term river behaviour. This chapter defines river training and explains its objectives, benefits, limitations, and key design principles. It discusses guide banks, groynes or spurs, embankments, revetments, pitching, launching aprons, cut-offs, and related protective works. It also considers structural placement, classification, construction materials, maintenance, and environmental consequences.