This document summarizes a study that developed a yield model using linear programming to optimize reservoir operations for the Isapur reservoir in India. The study aimed to calculate the optimal irrigation yield and compare it to actual irrigation releases. A linear programming model was formulated to maximize reservoir yield subject to water balance constraints. The model estimated over-year and within-year storage requirements separately. The results were analyzed to compare optimal yields from the model to actual historical irrigation releases and draw conclusions about reservoir performance.
ASSESSMENT OF LP AND GA AS RESERVOIR SYSTEM ANALYSIS TOOLSIAEME Publication
A reservoir is a huge manmade structure constructed for a number of reasons. It
uses natural water resources and helps in the development of a society. The quantum
of water in a reservoir is a function of the hydrologic characteristics of the region. An
efficient planning and operation of a reservoir is a skill of the water planner. The
works done by researchers in the system analysis of a reservoir are discussed in the
present paper. The most appreciated linear programming (LP) and genetic algorithm
(GA) are studied in the context of system analysis of Urmodi Reservoir in
Maharashtra, India. The objective function is set to minimize the sum of the squared
irrigation demand deficit. Results show that these tools seem to be versatile in nature
and efficiently adopted for reservoir operation purpose.
ASSESSMENT OF LP AND GA AS RESERVOIR SYSTEM ANALYSIS TOOLSIAEME Publication
A reservoir is a huge manmade structure constructed for a number of reasons. It
uses natural water resources and helps in the development of a society. The quantum
of water in a reservoir is a function of the hydrologic characteristics of the region. An
efficient planning and operation of a reservoir is a skill of the water planner. The
works done by researchers in the system analysis of a reservoir are discussed in the
present paper. The most appreciated linear programming (LP) and genetic algorithm
(GA) are studied in the context of system analysis of Urmodi Reservoir in
Maharashtra, India. The objective function is set to minimize the sum of the squared
irrigation demand deficit. Results show that these tools seem to be versatile in nature
and efficiently adopted for reservoir operation purpose.
Analysis and Characterization of Kainji Reservoir Inflow System_ Crimson Publ...CrimsonpublishersEAES
Analysis and Characterization of Kainji Reservoir Inflow System by Mohammed J Mamman*, Otache Y Matins and Jibril Ibrahim in Environmental Analysis & Ecology Studies
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Comparison and Evaluation of Support Vector Machine and Gene Programming in R...AI Publications
Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy. In this research, we have tried to evaluate sediment amounts, using Support Vector Machine (SVM), for Kashkanriver, Iran, and compare it with common Gene-Expression Programming. The parameter of flow discharge for input in different time lags and the parameter of sediment for output dhuring contour time (1998-2018) considered. Criteria of correlation coefficient, root mean square error, mean absolute error and Nash Sutcliff coefficient were used to evaluate and compare the performance of models. The results showed that two models estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the support vector machine model had the highest correlation coefficient (0.994), minimum root mean square error (0.001ton/day) , mean absolute error(0.001 ton/day) and the Nash Sutcliff (0.988) hence was chosen the prior in the verification stage. Finally, the results showed that the support vector machine has great capability in estimating minimum and maximum sediment discharge values.
OPTIMAL OPERATION OF SINGLE RESERVOIR USING ARTIFICIAL NEURAL NETWORKIAEME Publication
Optimal operating policies of a reservoir have been derived in deterministic and stochastic frame work as well as using Artificial Neural Network (ANN). With different combinations of input data set, five different ANN models have been developed. Out of five models, three models simulate
final storage and two models simulate optimal release. All these models are applied to develop optimal operating policies of Konar, a reservoir in Damodar Valley system in India. Based on data used as input variables, five types of ANN models are developed.
MODEL OF WATER BALANCE BASED ON THE SYSTEM DYNAMICS IAEME Publication
This research intends to investigate the relation between cause and effect which influence the water availability and water need, and then to build a formulation as an effort of intervention with high leverage. The object of this research is Batam island that is part of the Riau islands province-Indonesia. This province has been remained as the national strategy area based on the Government Regulation No 26/ 2008 about the spatial plan of national area. The methodology consists of the system dynamics approach that can integrate the complex and persistence system in analyzing water balance. In the system dynamics, the behaviour patterns are generated by the water availability and water need with increasing time and by using the main asumption that every complex system is sourced on the causal structure that is forming the system. The result is as the model of water balance due to the system dynamics generally in Indonesia and especially in Batam island
OPTIMAL RESERVOIR OPERATION FOR IRRIGATION OF CROPS USING GENETIC ALGORITHM: ...IAEME Publication
Genetic Algorithm is one of the global optimization schemes that have gained popularity as a means to attain water resources optimization. It is an optimization technique, based on the principle of natural selection, derived from the theory of evolution, is used for solving optimization problems.
In the present study Genetic Algorithm (GA) has been used to develop a policy for optimizing the release of water for the purpose of irrigation. The study area is Sukhi Reservoir project in Gujarat, India. The months taken for the case study are June, July, August and September for three years from
year 2004 to 2006.
Analysis and Characterization of Kainji Reservoir Inflow System_ Crimson Publ...CrimsonpublishersEAES
Analysis and Characterization of Kainji Reservoir Inflow System by Mohammed J Mamman*, Otache Y Matins and Jibril Ibrahim in Environmental Analysis & Ecology Studies
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Comparison and Evaluation of Support Vector Machine and Gene Programming in R...AI Publications
Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy. In this research, we have tried to evaluate sediment amounts, using Support Vector Machine (SVM), for Kashkanriver, Iran, and compare it with common Gene-Expression Programming. The parameter of flow discharge for input in different time lags and the parameter of sediment for output dhuring contour time (1998-2018) considered. Criteria of correlation coefficient, root mean square error, mean absolute error and Nash Sutcliff coefficient were used to evaluate and compare the performance of models. The results showed that two models estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the support vector machine model had the highest correlation coefficient (0.994), minimum root mean square error (0.001ton/day) , mean absolute error(0.001 ton/day) and the Nash Sutcliff (0.988) hence was chosen the prior in the verification stage. Finally, the results showed that the support vector machine has great capability in estimating minimum and maximum sediment discharge values.
OPTIMAL OPERATION OF SINGLE RESERVOIR USING ARTIFICIAL NEURAL NETWORKIAEME Publication
Optimal operating policies of a reservoir have been derived in deterministic and stochastic frame work as well as using Artificial Neural Network (ANN). With different combinations of input data set, five different ANN models have been developed. Out of five models, three models simulate
final storage and two models simulate optimal release. All these models are applied to develop optimal operating policies of Konar, a reservoir in Damodar Valley system in India. Based on data used as input variables, five types of ANN models are developed.
MODEL OF WATER BALANCE BASED ON THE SYSTEM DYNAMICS IAEME Publication
This research intends to investigate the relation between cause and effect which influence the water availability and water need, and then to build a formulation as an effort of intervention with high leverage. The object of this research is Batam island that is part of the Riau islands province-Indonesia. This province has been remained as the national strategy area based on the Government Regulation No 26/ 2008 about the spatial plan of national area. The methodology consists of the system dynamics approach that can integrate the complex and persistence system in analyzing water balance. In the system dynamics, the behaviour patterns are generated by the water availability and water need with increasing time and by using the main asumption that every complex system is sourced on the causal structure that is forming the system. The result is as the model of water balance due to the system dynamics generally in Indonesia and especially in Batam island
OPTIMAL RESERVOIR OPERATION FOR IRRIGATION OF CROPS USING GENETIC ALGORITHM: ...IAEME Publication
Genetic Algorithm is one of the global optimization schemes that have gained popularity as a means to attain water resources optimization. It is an optimization technique, based on the principle of natural selection, derived from the theory of evolution, is used for solving optimization problems.
In the present study Genetic Algorithm (GA) has been used to develop a policy for optimizing the release of water for the purpose of irrigation. The study area is Sukhi Reservoir project in Gujarat, India. The months taken for the case study are June, July, August and September for three years from
year 2004 to 2006.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Holistic Approach for Determining the Characteristic Flow on Kangsabati Cat...ijceronline
Kangsabati river rises from the Chotanagpur plateau in the state of West Bengal, India and passes through the districts of Purulia, Bankura and Paschim Medinipur in West Bengal before joining into river Rupnarayan. It is life of these three districts of West Bengal situated in the western part of the state. The river has ephemeral characteristics i.e. it has low flow in the year round and have a high peak on a certain time basis. In the Kangasabati catchment hydrological study gives an evident that during the period every two years there is a chance of drought condition and consecutively after that there is a high flow year. In our study period from 1991 to 2010 there are six low streamflow year i.e. in that year there is less rainfall than the average rainfall on that area. The year 1991, 2002 and 2009 are the drought prone year and above that in 2010 the severe drought condition was seen and this is the lowest rainfall year among the last 20 years and the rainfall on this year is only 766 mm which is in an about 38% less rainfall than the average rainfall of the catchment. And the highest flood peak in the last twenty year is noted on 19th Aug 2007 as 377107.8 Mm3
Comparison of Explicit Finite Difference Model and Galerkin Finite Element Mo...AM Publications
This paper describes Galerkin finite element (FEFLOW) models for the simulation of groundwater flow in twodimensional,
transient, unconfined groundwater flow systems. This study involves validation of FEFLOW model with reported
analytical solutions and also comparison of reported Explicit Finite Difference Model for groundwater flow simulation
(FDFLOW). The model is further used to obtain the space and time distribution of groundwater head for the reported
synthetic test case. The effect of time step size, space discretizations, pumping rates is analyzed on model results.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
DEVELOPMENT OF CLEAN WATER DISTRIBUTION NETWORK CAPACITY BY USING WATERCADIAEME Publication
In this study a network model was constructed for the hydraulic analysis and
design of a small community (Kedungkandang District) water distribution network in
East Java Province of Indonesia by using Water cad simulator. The analysis included
a review of pressures, velocities and head loss gradients under steady state average
day need. The clean water availability in the location study is 560 l/s, however the
local society that is 23,213 consumers can only use in amount of 116 l/s. The
assessment of existing condition due to the pipe hydraulic condition and the
development of capacity network increasing are carried out by using the program of
Water cad vs. XM Edition. The development condition consists of 27,284 populations.
Result indicates that the average discharge need is 41.763 l/s, however in the peak
hour need there is needed 65.150 l/s on 2031. The water pressure in the development
area is 2.3 atm on 06.00 am
Abstract Urban watersheds produce an instantaneous response to rainfall. That results in stormwater runoff in excess of the capacity of drainage systems. The excess stormwater must be managed to prevent flooding and erosion of streams. Management can be achieved with the help of structural stormwater Best Management Practices (BMPs). Detention ponds is one such BMP commonly found in the Austin, TX, USA. The City of Austin developed a plan to mitigate future events of flooding and erosion, resulting in the development and integration of stormwater BMP algorithms into the sub-hourly version of SWAT model. This paper deals with the development of a physically based algorithm for detention pond. The algorithm was tested using a previously flow-calibrated watershed in the Austin area. From the test results obtained it appears that the detention pond algorithm is functioning satisfactorily. The algorithm developed could be used a) to evaluate the functionality of individual detention pond b) to analyze the benefits of such structures at watershed or higher scales and c) as design tool. Keywords: flooding, detention, urban, watershed, BMP, algorithm, stormwater, modeling
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
2. International Journal of Civil Engineering Research and Development (IJCERD), ISSN 2228-9428(Print)
ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
22
reservoir system operation and to capture the desired reliability of target releases considering
the entire length of the historical flow record. The yield model estimates over-year and
within-year reservoir capacity requirements separately to meet the specified release reliability
targets. Over-year capacity is governed by the distribution of annual streamflows and the
annual yield to be provided. The maximum of all over-year storage volumes is the over-year
storage capacity. Any distribution of within-year yields that differs from the distribution of
the within-year inflows may require additional active reservoir capacity. The maximum of all
within-year storage volumes is the within-year storage capacity. The total active reservoir
storage capacity is simply the sum of the over-year storage and within-year storage
capacities.
The concept of a yield model was introduced by Loucks et al.(1981); Stedinger et
al.(1983) reviewed and compared deterministic, implicitly stochastic, and explicitly
stochastic reservoir screening models. Loucks et. al. (1981) demonstrated that in several
cases the yield model provides a reasonable estimate of the distribution of reservoir capacity
requirements obtained with the sequent peak algorithm.
Dandy G.C. and Connarty M.C. and Loucks D.P. (1997) made a comparison of
simulation, network linear programming, full optimization LP model and the LP yield model
for estimating the safe yield of the Canberra water supply system consisting of four
reservoirs. They pointed out that, although a simulation model will accurately assess the
system yield for an assumed set of operating rules, it will not assess the maximum yield that
can be achieved by adopting the best possible set of operating rules for the system.
Dahe P.D. and Srivastava D.K. (2002) developed the basic yield model and present a
multiple yield model for a multiple reservoir system consisting of single purpose and
multipurpose reservoirs. The objective is to achieve pre specified reliabilities for irrigation
and energy generation and to incorporate an allowable deficit in the annual irrigation target.
The results are analyzed for four cases. the real shortfalls between demand and flow are
encountered during certain seasons or months of the year whereas on a year by year basis ,
the total demand is much lower than the minimum annual flow in the river. Such reservoirs
are known as within-year systems.
Srivastava D.K and Taymoor A. Awachi (2009) develops nested models were applied
in tandem using linear programming (LP), dynamic programming (DP), artificial neural
networks (ANN), hedging rules (HRs), and simulation. An LP-based yield model(YM) has
been used to reevaluate the annual yields available from the Mula reservoir for water supply
and irrigation.
This study presents a methodology to optimize the design of the single reservoir irrigation
system by taking monthly inflow and initial storage and tries to predict the maximum
possible releases using Linear programming based Yield model. The specific objectives of
the present study can be stated as fallows:
1. To develop a Linear Programming based yield model for reservoir operation for a
monthly time step.
2. Comparison of yield model and actual irrigation releases for single purpose irrigation
Isapur reservoir.
3. To draw the conclusions from the interpretation of results obtained.
Reservoir Yield Model
The conceptualisation and details of the yield model on which the present model
development is based are presented in Loucks et. al. (1981, pp 339-353, 368-371). When
reservoir yield with reliability lower than the maximum reliability is to be determined, the
extent of availability of yield (or the allowable deficit in yield) during failure years can be
3. International Journal of Civil Engineering Research and Development (IJCERD), ISSN 2228-9428(Print)
ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
23
specified. This is achieved by specifying a failure fraction for the yield during the failure
years. The factor θp,j is used in the model to define the extent of available yield during failure
years. The objective of this model is to maximize the yield for given capacity of the reservoir.
Let p denotes the exceedence probability for the yield. The index j refers to a year and index t
refers to a within-year period. In this model only the firm yield is used.
The yield model is given by Dahe and Srivastava (2002) to determine single yield
from a reservoir is as follows.
The formulation of the yield model is as follows:
Objective function
Maximize Oy f, p
(1)
Constraint
1. Over-year storage continuity
f, po o
j jp, jj jj-1
SpI Oy Els s−−+ − =θ ∀ j (2)
The over-the-year capacity is governed by the distribution of annual stream flows and
the annual yield to be provided. The maximum of all the over-the-year storage volumes is the
over-the-year storage capacity. It is possible to specify a failure fraction to define the
allowable deficit in annual reservoir yield during the failure years in a single-yield problem.
In the above equation, Oy f, p
is the safe (firm) annual yield from reservoir with reliability p.
o
j-1s and
o
js are the initial and the final over-the-year active storages in year j, respectively;
jI is the inflow in year j; θp,j is the failure fraction defining the proportion of the annual yield
from reservoir to be made available during the failure years to safeguard against the risk of
extreme water shortage during the critical dry periods (θp,j lies between 0 and 1, i.e., for a
complete failure year θp,j =0, for a partial failure year 0 < θp,j <1, and for a successful year θp,j
=1); jSp excess release (spills) in year j; and jEl = evaporation loss in year j.
2. Over-year active storage volume capacity
o
j-1 Ys ≤ ∀ j (3)
The active over-year reservoir capacity (Y) required to deliver a safe or firm annual
yield.
3. Within-year storage continuity
w tf, p wt
t-1 t tf, pt
t
β OyOy El Els s
+ −+ − =
∑ ∀ t (4)
Any distribution of the within-the-year yields differing from that of the within-the-
year inflows may require additional active reservoir capacity. The maximum of all the within-
the year storage volumes is the within-the-year storage capacity. In the above equation,
w
t-1s
and
w
ts are the initial and the final within-the-year active storages at time t; tβ is the ratio of
the inflow in time t of the modeled critical year of record to the total inflow in that year; and
tEl is the within-the-year evaporation loss during time t. The inflows and the required
releases are just in balance. So, the reservoir neither fills nor empties during the critical year.
4. Definition of estimated evaporation losses
4. International Journal of Civil Engineering Research and Development (IJCERD), ISSN 2228-9428(Print)
ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
24
w w
0 rt-1 t
j j-1 t
E1 E0 + El2
s ss t
γ
+
= +
∑ ∀ j (5)
Estimated annual evaporation losses from reservoir.
5. Definition of estimated evaporation losses
w w
t o rt-1 t
c r t
E0 +E1 El2
s sstγ γ
+
= +
∀ t (6)
The initial over year storage volume in the critical year s
o
rc
is assumed to be zero.
Estimated within-the-year evaporation losses from reservoir.
6. Total reservoir capacity
w
t-1 YaY s+ ≤ ∀ t (7)
Sum of the over-the-year and the within-the-year storage capacities is equal to the
active storage capacity of the reservoir.
7. Proportioning of yield in within-year periods
( )t f, p
f, pOy OytK= ∀ t (8)
Kt defines a predetermined fraction of reservoir yield for the within-year yield in
period t.
The equation 1 to 8 presents the single reservoir yield model.
SYSTEM DESCRIPTION: ISAPUR RESERVOIR
The Penganga River is the largest southern flowing river in the Godavari Basin
located in Akola, Buldhana, Hingoli, Parbhani, Nanded, Yeotmal districts of Maharashtra
states in INDIA. The system of Upper Penganga Project- Isapur Reservoir is considered in
this study. It is the major irrigation reservoir with live capacity of 958.43 MCM and Gross
Storage capacity of reservoir is 1241.43 MCM. The monthly flow data of 28-years (1982-
2009) for Upper Penganga reservoir- Isapur Dam is considered for analysis Table 1 is the
silent features of Upper Penganga Project- Isapur reservoir.
Table 1. Silent features of Upper Penganga Project- Isapur reservoir
Scope of Scheme Irrigation Purpose
Location Penganga river at Isapur
Catchment area 4636 Sq Km
Mean annual inflow (1982-2009) 670.98 MCM
Gross storage capacity 1241.43 MCM
Capacity of Live Storage 958.43 MCM
Capacity of Dead Storage 283.00 MCM
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ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
25
28 years historic inflow data for the system considered is available as shown in Figure
1, the maximum inflow of river 3179.05 MCM was recorded in the year 1988 and minimum
inflow was 88.70 MCM was recorded in the year 2004.
Figure: 1 Penganga river Inflow at Isapur Reservoir
Irrigation parameters (Kt) of Isapur Reservoir
The monthly proportions of the annual irrigation targets (Kt values) are worked out by
considering the cropping patterns and irrigations intensities recommended by the agricultural
officer. Kt defines a predetermined fraction of reservoir yield for the within-year yield in
period t. The Kt values are given in Table 2 .and shown in Figure 2 .
Approximation of critical within-year inflows (βt) values of Isapur Reservoir:
βt based on average monthly flows. The βt values based on average monthly flows for
reservoir are given in Table 2 and shown in Figure 3.
Evaporation parameters of Reservoirγ t
:
The average monthly evaporation depth at all the reservoirs is obtained from the Water
Resources Department and available project reports. The evaporation volume loss due to
dead storage E0= 64.67 is obtained by product of the average annual evaporation depth and
the area at dead storage elevation for respective reservoirs. The storage-area and storage-
elevation relationship is taken for study. A linear fit for the storage-area data for each
0
200
400
600
800
1000
1200
1400
1600
1800
1982-1
11
21
31
41
51
1987-61
71
81
91
101
111
1992-121
131
141
151
161
171
1997-181
191
201
211
221
231
2002-241
251
261
271
281
291
2007-301
311
321
331
InflowMCM
Months( June 1982 to May 2010)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Ktvalues
Time period
Figure: 2. Values of Kt for UPP
Isapur reservoir
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
6 7 8 9 10 11 12 1 2 3 4 5
βtvalues
Time period
Figure: 3. Values of βτ for UPP Isapur
reservoir
6. International Journal of Civil Engineering Research and Development (IJCERD), ISSN 2228-9428(Print)
ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
26
reservoir above the dead storage is obtained from the storage area relationship. The
evaporation volume loss rate r
El = 0.1172 is obtained by taking the product of the slope of
the area elevation curve linearized above dead storage and the average annual evaporation
depth at respective reservoirs. The parameter γt (the fraction of the annual evaporation
volume loss that occurs in within-year period t) is computed by taking the ratio of the average
monthly evaporation depth to the average annual evaporation depth at respective reservoirs.
The values of the γt are given in the Table 2 and shown in Figure 4.
Table: 2 within-year inflow approximation, Irrigation and evaporation parameters used in the
yield model for Isapur Reservoir in Penganga river.
Month June July August September October November
βt 0.0812 0.2044 0.3105 0.2498 0.1193 0.0172
γt 0.0976 0.0729 0.0611 0.0638 0.0604 0.0600
Kt 0.0076 0.1103 0.0894 0.1085 0.0700 0.1466
Edepth (m) 0.1847 0.1380 0.1156 0.1207 0.1144 0.1135
Month December January February March April May
βt 0.0083 0.0037 0.0020 0.0013 0.0011 0.0012
γt 0.0544 0.048 0.0802 0.1109 0.1319 0.1588
Kt 0.1165 0.1083 0.0613 0.0312 0.0428 0.1075
Edepth (m) 0.1029 0.0910 0.1517 0.2088 0.2495 0.3004
ANALYSIS AND RESULTS
Application of the Yield Model in Isapur reservoir:
The observed historical inflows for 28 years (1982-2009) at the Isapur reservoir were
used in computation of the yields from the reservoir with an active capacity of 958.43 MCM
(project capacity). Out of these a set of 6 lowest flow years (≈ 25 % of the years) were
assumed as the failure years, determined by the modified method of determining failure years
by yield model. Thus remaining 22 years were successful years representing 75% annual
project reliability. The six failure years are (22nd
, 23rd
, 24th
, 26th
, 27th
and 28th
) 2003,
2004,2005,2007,2008 and 2009. With the provision of θp,j , the extent of failure in the annual
yield from the reservoir during failure years was monitored as clear guidelines were not
established for deciding its value. The value of θp,j for the project was determined using the
YM with an objective to minimize its value. In single purpose reservoir, irrigation originally
being the main project target was considered as a single yield or firm yield from the reservoir.
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
6 7 8 9 10 11 12 1 2 3 4 5
γtvalues
Time period
Figure : 4 Values of γt for UPP Isapur reservoir
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ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
27
The annual project reliability for irrigation was kept equal to 75%. The value of θp,j was found
to increase with the decrease in the annual yield from the reservoir.
For Isapur reservoir capacity of 958.43 MCM, yield is found out for Safe reservoir
yield (θp,j=1), θp,j=0.25, θp,j=0.50 and θp,j= 0.00 respectively and calculated annual yield of
reservoir by yield model is 364.20, 454.38, 527.85 and 527.85 MCM respectively.
As per yield model analysis the firm yield is found that for 75 % reliability with 50 %
allowable deficit (θp,j=0.50) and 75 % reliability with 100% allowable deficit(θp,j=0.00) is
same as 527.85 MCM, The value of θp,j adopted for the project was 0.5, this gives less spill
and higher utility of flow. Hence for the critical periods we can achieve at least 50 % of
irrigation target releases. Within-period water releases are shown in table 3.
Table: 3. Representing the monthly water releases for irrigation by approximate YM.
Month June July August September October November
Safe Reservoir Yield 2.75 40.16 32.57 39.52 25.49 53.38
θp,j =0.25 3.43 50.10 40.63 49.31 31.80 66.60
θp,j =0.50 3.99 58.20 47.20 57.29 36.95 77.37
θp,j =0.00 3.99 58.20 47.20 57.29 36.95 77.37
Month December January February March April May
Safe Reservoir Yield 42.43 39.42 22.31 11.35 15.58 39.20
θp,j =0.25 52.93 49.18 27.84 14.17 19.44 48.90
θp,j =0.50 61.49 57.14 32.34 16.46 22.58 56.81
θp,j =0.00 61.49 57.14 32.34 16.46 22.58 56.81
Comparison of YM and Actual Releases in Isapur Reservoir:
The main objective is to compute the yield that should be released to fulfill the total
demand. Comparison of actual demand, releases and yield which we are getting from the
model used are as follows. Yield model based on the monthly inflow and monthly irrigation
demands of the reservoir operation system is considered for the comparison.
Table 4: Values of Actual Demand, Actual releases and Yield Model (YM with 75% reliable θp,j=0.50)
Table 2 gives the output of the model used for 75 % reliable yield as well as demand
and actual releases in the years which are considered. The data available of only 11 years is
used for comparison. As per the Table no. 4 the actual releases from the reservoir is
maximum 562.260 MCM in the year 2002-2003 and minimum is 67.245 MCM in year 2009-
2010. Actual releases are not constant for the years considered for comparison and some of
Month YM Demand
Actual Water Releases in years 1999 to 2009 Average
Water
release
99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10
June 3.99 5.66 1.306 1.666 1.646 1.871 1.276 0.393 1.030 1.362 1.483 1.271 0.791 1.281
July 58.20 82.59 0.201 1.257 0.254 0.289 0.197 0.061 0.159 0.210 1.229 0.196 0.178 0.385
Aug 47.20 66.98 0.196 0.250 1.747 0.281 0.192 0.059 0.155 0.205 0.223 0.191 0.221 0.338
Sept 57.29 81.29 0.181 0.231 0.228 0.259 0.177 0.054 0.143 0.189 0.205 0.176 0.171 0.183
Oct 36.95 52.43 0.176 0.224 0.221 0.252 0.172 0.053 0.139 0.183 1.200 0.171 0.171 0.269
Nov 77.37 109.79 55.835 64.837 64.064 76.816 41.673 15.276 40.096 57.020 54.735 49.467 0.205 47.275
Dec 61.49 87.26 68.814 87.768 86.221 98.569 67.241 22.679 54.277 71.771 82.153 66.962 1.139 64.327
Jan 57.14 81.08 64.040 84.680 83.706 91.731 71.577 21.245 52.512 66.792 72.733 62.317 0.503 61.076
Feb 32.34 45.89 61.342 80.616 79.607 95.028 67.826 18.937 50.328 64.193 66.347 64.557 8.380 59.742
March 16.46 23.36 41.580 48.033 45.400 59.559 40.630 13.495 28.796 38.367 55.224 40.461 8.066 38.146
April 22.58 32.05 51.716 65.960 65.174 70.077 46.534 11.541 44.791 53.938 51.735 50.324 0.774 46.597
May 56.81 80.61 47.144 65.129 66.412 67.528 46.066 14.167 37.184 55.170 58.543 45.875 46.646 49.988
Yield 527.85 748.99 392.531 500.651 494.680 562.260 383.561 117.960 309.610 409.400 445.810 381.968 67.245 369.607
8. International Journal of Civil Engineering Research and Development (IJCERD), ISSN 2228-9428(Print)
ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
28
the years are near to 75% reliable yield i.e 536.45 MCM in years 2000-01, 2001-2002, and
2002-2003. Whereas the actual releases are very less in the remaining years than the 75 %
reliable yield by yield model analysis. Because of which the average water released is very
less as compared to 75 % reliability yield.
Figure 6 shows comparison between monthly water releases, monthly demand and
monthly yield by yield model . From the figure it is very clear that in the month of June,
December and January the reservoir releases are near to the yield model, where as the actual
demand is very large as compared to the actual releases from the reservoir except in the
month February, March and April. It can be seen from the Figure 5 that the releases are
negligible in the period of Kharif Crop i.e June, July, August, September and mid of October.
Whereas the releases are more in the period of Rabbi Crop (i.e from October to February) and
in Hot Weather crop period (i.e from February to May). As per project report they have
considered releases in the month of June to October but actual releases are negligible
considering due to monsoon periods.
Actual releases are considered as constant fixed quantity depending upon local demand for
irrigation purposes and not on climatological conditions or crop variations that’s why these
actual irrigation releases are not equal to the demand.
The Yield model can be used for yield assessment with specified reliabilities and thus
assists in the effective management and design of irrigation reservoir system. Yield model
provides a better alternative to the deterministic full optimization model by the way of
reduction in size and sufficiently accurate results. It also allows determination of annual yield
with a given reliability less than the maximum reliability. There is also a provision of
determining the percentage of annual yield to be supplied during failure years.
Figure: 5 Comparison of Actual demand, Actual Releases and Yield Model
0
20
40
60
80
100
120
6 7 8 9 10 11 12 1 2 3 4 5
IrrigationReleasesinMCM
Month
YM Demand Actual Release
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ISSN 2248 – 9436(Online), Volume 1, Number 2, May-October (2011)
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CONCLUSION
The Isapur reservoir is analysed with the yield model to find their annual irrigation
targets. The yield model employs monthly flows for 28 years data and is capable of
permitting shortages in the annual targets of failure years. Reservoir is analyzed to find its
annual irrigation targets with 75 % annual project dependability with failure fractions of zero,
0.25, 0.50 and 0.00. The failure years (22nd
, 23rd
, 24th
, 26th
, 27th
, and 28th
) 2003, 2004, 2005,
2007, 2008 and 2009 are maintained in all the analysis. As per yield model analysis the firm
yield is found that for 75 % reliability with 50 % allowable deficit and 75 % reliability with
100% allowable deficit is same as 527.85 MCM, hence for the critical periods we can achieve
at least 50 % of irrigation target supply of the above yield i.e 263.92 MCM.
It can be concluded that yield model performs better than the actual irrigation release.
The Yield model gives accurate result by considering the monthly evaporation without
increasing the size of the model. There is also a provision of determining the percentage of
annual yield to be supplied during failure years.
The choice of method of analysis and model shall depend upon factors like the nature
of study, its purpose and the size of problem. Yield model is relatively superior as it can
consider the reliability of annual yields as well as the allowable deficit during failure years.
The simulation model improves results of optimization model. Therefore using of simulation
model is necessary after optimization.
REFERENCES
1. Chaturvedi, M. C. and Srivastava, D. K. (1981). “Study of a complex water resource
system with screening and simulation models.” Water Resources Research, vol.17 no.4, pp.
783-794.
2. Dandy, G.C., and Connarty, M.C. and Loucks, D.P. (1997). “Comparison of Methods
for Yield Assessment of Multipurpose Reservoir Systems”, ASCE, Journal of WRPM,
vol.123, no.6, pp. 350-358.
3. Dahe, P.D., and Srivastava, D.K. (2002). “Multipurpose multiyield model with
allowable deficit in annual yield”, ASCE, Journal of WRPM, vol.128, no.6, pp. 406-414.
4. Hall, W.A., and Dracup, J.A. (1970). Water Resources System Engineering. McGraw
Hill Inc., New York, U.S.A.
5. Loucks, D. P., and O.T. Sigvaldason, (1982). Multiple-reservoir operation in North
America, in The Operation of Multiple Reservoir systes, edited by Z. Kaczmareck and J.
Kindler, International Institute for Applied Systems Analysis, Laxenburg, Austria.
6. Loucks, D. P., Stendiger, J. R. and Haith, D. A. (1981). Water resource systems
planning and analysis. Prentice-Hall, Inc., Englewood Cliffs, N. J.
7. Simonovic, S.P. (1992). “Reservoir system analysis : Closing gap between theory and
practice.” J. Water Resour. Plng. And Mgmt., ASCE, 118(3), 262-280.
8. Srivastav, D.K., and Awchi T.A. (2009) . “Storage-Yield evaluation and operation of
Mula reservoir, India.” J. Water Resour. Plug. And Mgmt., ASCE, 135(6), 414-425.
9. Wurbs, R.A. (1993). “Reservoir management and operation models.” J. Water
Resour. Plng. And Mgmt., ASCE, 119(4), 455-472.
10. William W. G. Yeh (1985),”Reservoir Management and Operation s Models: A State-
of-the-Art Review”, Water Resources Research, vol 21, no.12, pp. 1797- 1818.