"Dünyada herkese yetecek kadar kaynak var, ancak herkesin hırsını karşılamaya yetecek kadar değil."  Mahatma GANDİ

Genel

Integrated Water Resources Management using Artificial Intelligence

 

Author: Devrishi Bharadwaj

“Water is the livelihood of our bodies, our economy, our nation

and our well-being.” –Stephen Johnson

 

 

Water is an indispensable part of life. Proper scientific management of water

resources is a must to meet the demands of the rising population and to

ensure its availability for our future generations. Hydrological processes have

significant influence on the ecology of earth. Development of water resources

can greatly benefit communities, ensure sustainable development and

significantly contribute to the economy of a region, especially at river basins

and coastal plains.

Management of water resources is a complex process because of great

variations in rainfall patterns; flow of rivers, weather and climate, and the

whole hydrological process is a dynamic phenomenon. It is also influenced by

socio-economic factors such as consumption levels, location of industries and

townships, festivals etc. With many parts of India facing severe water scarcity,

the alarm bell has rang for planning out efficient, scientific and a real time

water resources management system. It should take into account all the

factors of water resources management, to meet the rising demand of India’s

increasing population and economy but at the same time ensure no

disturbance in the ecological cycle. Water is life, so availability of freshwater is

a must for the continuity of the human civilization.

This article explains only the applications of ANN, GA and GIS in the theoretical

way, not the mathematical formulas, equations and computer programming

methods involved. Also this article specially focusses on use of AI in planning &

managing Water Distribution Systems.

Conventional mathematical and statistical models have failed to give optimal

solutions, predictions and simulations to aid in the management of water

resources. Although there has been a great amount of research being carried

out in India, on the branches of Artificial Intelligence (AI) such as Artificial

Neural Networks (ANN) and Genetic Algorithms (GA), but its application has

remained very limited. ANN and GA can be applied to solve, simulate and

predict almost any problem but this article focuses on the use of ANN and GA

with GIS on the management and development of water resources in India,

which will have revolutionary scientific, social and economic benefits for the

nation.

 

 

 

Artificial Neural Networks - The ANN is a computational approach to complex

problems inspired by the biological nervous system. It has adaptive learning

capabilities, can recognize patterns and learn from their interactions. Both

historical data and real time data can be integrated in ANN. ANN give the best

optimal solution, simulation or prediction of a given problem.

Genetic Algorithms- An algorithm is any procedure that takes in data and

modifies it according to a step-by-step set of instructions. GAs simulate the

logic of the Darwinian theory of Natural Selection to solve complex problems.

Holland gave the concept of GAs in 1970s. GAs can also be called self-learning

algorithms.

Geographic Information System- A geographic information system (GIS) is a

computer system for capturing, storing, checking, and displaying data related

to positions on Earth’s surface. By relating seemingly unrelated data, GIS can

help individuals and organizations better understand spatial patterns and

relationships.

Due to stochastic parameters and uncertainties in the water resources system,

GA and ANN will be helpful as they have adaptive learning capabilities. They

are useful for complex optimization problems, where the number of

parameters is large and analytical solutions are difficult to obtain. Integrated

use of surface and ground water is essential for optimum utilization of water

resources. In most models, the actual interaction between surface and

groundwater is often neglected. Conjunctive use of surface and groundwater is

important. ANN based simulation models can be used in GA based

optimization models to provide sector wise optimal water allocation. This data

can be presented in GIS for easy understanding of decision makers, and also

automated systems aided by human interaction can be developed. ANN has

greater advantage in case of assessing and simulation ground water resources

than software such as PMWIN (Processing MODFLOW for Windows). ANN &

GA can be used to optimize hydropower generation and at the same time

develop an efficient irrigation system from a multipurpose dam. There are a

number of important areas that have to be studied in detail to develop an

Integrated Water Resources Management System such as Water Distribution

Systems, Sewage Management, Irrigation, Natural & Artificial Floods, Dams &

Reservoirs, Ground & Surface Water, Rainfall & Climatic Factors, Local

Hydrology of a particular area, etc.

From a hydraulic point of view, water supply system is a complex system,

which needs to meet the demand continuously. Since a modification of a single

parameter like pipe diameter, water pressure, pipe layout can have an impact

on the entire system. Therefore a thorough estimation is required. The

situation in case of firefighting, low rainfall, demand during peak hours, pipe

leakage, damaged pumps etc. are important factors. These all parameters can

be taken care by ANN and GA. Water at a required pressure have to be

supplied continuously but water supplying authorities also want to save costs

and make the system more efficient. ANN and GA make harmonisation of

interests and requests possible.

GA can solve combinatorial optimisation problems, which cannot be solved by

conventional operational research methods. The GA provides results which are

within reasonable limits of deviation from the real situation, unlike the

conventional methods whose results do not match the actual situation. Also a

single GA can be used for a range of desired objectives, because of its multiobjective

function capability. Through the integrated system, pumping cost can

be reduced and ground water table variations can be controlled. In some

areas, pipes may not be necessary (which can be planned using GA) which will

reduce the construction and maintenance cost. Using GIS is effective to

determine maintenance works in water supply systems, which is essential to

ensure provide sufficient water to the citizens and to maintain longer lifeline of

the system. Water Distribution Systems are comprised of interconnected

sources such as pipes and hydraulic control elements (pumps, valves,

regulators, tanks etc). Using this integrated system, water distribution/supply

systems can be designed to deliver sufficient quantity of water to consumers at

required pressure, quality (safe) and in a continuous, cost effective and

sustainable manner. Therefore using ANN and GA based models coupled with

GIS is important for optimal design of a water distribution network to minimize

construction costs while ensuring adequate system performance under

specified design criteria.

Water Resources management and design problems often involve political,

social, and other subjective goals that cannot be represented mathematically.

But by coupling an ANN simulation model with a GA based optimisation

approach, a mathematically optimal solution may be identified. GAs can even

help in identification of important factors which are not clearly determined.

GAs are flexible for the ever changing dynamics of water resources

management. There is a developed methodology for a GA model calibration

with interactive evolution to incorporate un-modelled objectives (or objectives

that remain unknown to the policy makers) in the search procedure, which is

not possible in conventional methods.

Interactive evolution aims to utilize subjective responses from human users to

drive the evolutionary search. Human responses are given numerical ranks and

then used as fitness function for the GA. The computational limits posed by

fitness evaluation of GA have paved the way for development of

approximation frameworks, where by the process of ANN coupled evolution

and online training of an ANN (means the ANN is learning during the

operational process of the GA) to adaptively classify the trustworthiness of the

approximation model and enhance its performance.

Development of a an Integrated Water Resources System for India linking

multiple ANNs, GAs (sector & purpose wise) presenting it in a GIS or other

suitable software will not only help policy makers to make optimum and

sustainable use of the precious water resources, but will also lead to automatic

cost-effective water distribution systems and aid in the overall socio-economic

growth of the nation.

(The author is the Chairman of Bharat Innovates Universals)

Published by Department of Research & Publications, Bharat Innovates

Universals

Read our articles at www.biutransformingindia.blogspot.com

 

Artificial Neural Networks - The ANN is a computational approach to complex

problems inspired by the biological nervous system. It has adaptive learning

capabilities, can recognize patterns and learn from their interactions. Both

historical data and real time data can be integrated in ANN. ANN give the best

optimal solution, simulation or prediction of a given problem.

Genetic Algorithms- An algorithm is any procedure that takes in data and

modifies it according to a step-by-step set of instructions. GAs simulate the

logic of the Darwinian theory of Natural Selection to solve complex problems.

Holland gave the concept of GAs in 1970s. GAs can also be called self-learning

algorithms.

Geographic Information System- A geographic information system (GIS) is a

computer system for capturing, storing, checking, and displaying data related

to positions on Earth’s surface. By relating seemingly unrelated data, GIS can

help individuals and organizations better understand spatial patterns and

relationships.

Due to stochastic parameters and uncertainties in the water resources system,

GA and ANN will be helpful as they have adaptive learning capabilities. They

are useful for complex optimization problems, where the number of

parameters is large and analytical solutions are difficult to obtain. Integrated

use of surface and ground water is essential for optimum utilization of water

resources. In most models, the actual interaction between surface and

groundwater is often neglected. Conjunctive use of surface and groundwater is

important. ANN based simulation models can be used in GA based

optimization models to provide sector wise optimal water allocation. This data

can be presented in GIS for easy understanding of decision makers, and also

automated systems aided by human interaction can be developed. ANN has

greater advantage in case of assessing and simulation ground water resources

than software such as PMWIN (Processing MODFLOW for Windows). ANN &

GA can be used to optimize hydropower generation and at the same time

develop an efficient irrigation system from a multipurpose dam. There are a

number of important areas that have to be studied in detail to develop an

Integrated Water Resources Management System such as Water Distribution

Systems, Sewage Management, Irrigation, Natural & Artificial Floods, Dams &

Reservoirs, Ground & Surface Water, Rainfall & Climatic Factors, Local

Hydrology of a particular area, etc.

From a hydraulic point of view, water supply system is a complex system,

which needs to meet the demand continuously. Since a modification of a single

parameter like pipe diameter, water pressure, pipe layout can have an impact

on the entire system. Therefore a thorough estimation is required. The

situation in case of firefighting, low rainfall, demand during peak hours, pipe

leakage, damaged pumps etc. are important factors. These all parameters can

be taken care by ANN and GA. Water at a required pressure have to be

supplied continuously but water supplying authorities also want to save costs

and make the system more efficient. ANN and GA make harmonisation of

interests and requests possible.

GA can solve combinatorial optimisation problems, which cannot be solved by

conventional operational research methods. The GA provides results which are

within reasonable limits of deviation from the real situation, unlike the

conventional methods whose results do not match the actual situation. Also a

single GA can be used for a range of desired objectives, because of its multiobjective

function capability. Through the integrated system, pumping cost can

be reduced and ground water table variations can be controlled. In some

areas, pipes may not be necessary (which can be planned using GA) which will

reduce the construction and maintenance cost. Using GIS is effective to

determine maintenance works in water supply systems, which is essential to

ensure provide sufficient water to the citizens and to maintain longer lifeline of

the system. Water Distribution Systems are comprised of interconnected

sources such as pipes and hydraulic control elements (pumps, valves,

regulators, tanks etc). Using this integrated system, water distribution/supply

systems can be designed to deliver sufficient quantity of water to consumers at

required pressure, quality (safe) and in a continuous, cost effective and

sustainable manner. Therefore using ANN and GA based models coupled with

GIS is important for optimal design of a water distribution network to minimize

construction costs while ensuring adequate system performance under

specified design criteria.

Water Resources management and design problems often involve political,

social, and other subjective goals that cannot be represented mathematically.

But by coupling an ANN simulation model with a GA based optimisation

approach, a mathematically optimal solution may be identified. GAs can even

help in identification of important factors which are not clearly determined.

GAs are flexible for the ever changing dynamics of water resources

management. There is a developed methodology for a GA model calibration

with interactive evolution to incorporate un-modelled objectives (or objectives

that remain unknown to the policy makers) in the search procedure, which is

not possible in conventional methods.

Interactive evolution aims to utilize subjective responses from human users to

drive the evolutionary search. Human responses are given numerical ranks and

then used as fitness function for the GA. The computational limits posed by

fitness evaluation of GA have paved the way for development of

approximation frameworks, where by the process of ANN coupled evolution

and online training of an ANN (means the ANN is learning during the

operational process of the GA) to adaptively classify the trustworthiness of the

approximation model and enhance its performance.

Development of a an Integrated Water Resources System for India linking

multiple ANNs, GAs (sector & purpose wise) presenting it in a GIS or other

suitable software will not only help policy makers to make optimum and

sustainable use of the precious water resources, but will also lead to automatic

cost-effective water distribution systems and aid in the overall socio-economic

growth of the nation.

(The author is the Chairman of Bharat Innovates Universals)

Published by Department of Research & Publications, Bharat Innovates

Universals

Read our articles at www.biutransformingindia.blogspot.com

Yorumlarınızı Bizimle Paylaşın

Sadece üyelerimiz yorum yapabilir, hemen ücretsiz üye olmak için Tıklayın

(E-Posta adresiniz yayınlanmayacaktır)
Yorumu Gönder
Henüz Yorum Yapılmamış

Ziyaretçi İstatistikleri

Aktif ziyaretçi sayısı: 4 Bugünkü ziyaretçi sayısı: 205 Toplam tekil ziyaretçi sayısı: 98989