Water Demand Planning Research Articles

The Relationship between Integrated Development Planning and Water Demand Planning

The correlation between integrated development planning and water demand planning represents innovative and noteworthy concepts within the field of public administration. In recent years, a significant number of researchers have focused their investigations on elucidating the connection between integrated development planning and water demand Planning, offering a fresh perspective on public management. These processes constitute vital elements of contemporary public management within governmental sectors. This study employs a mixed-method research approach, incorporating both qualitative and quantitative methodologies. Secondary data, obtained through a systematic review of literature comprising 237 documents from Scopus-indexed journals spanning the years 2019 to 2024, provides insights into the extensive body of literature concerning the relationship between integrated development planning and water demand planning. Primary data was collected from five interviews conducted with managers from the Vhembe District Municipality. The literature review analysis reveals that the research based on the relationship between integrated development planning and water demand planning is fragmented, with different subsets of documents focusing on distinct aspects or methodologies without much overlap or integration between them. The abundance of literature on the relationship between integrated development planning and water demand planning underscores its significance in the field of public management. Insights from interviews indicate a lack of clarity regarding roles and responsibilities concerning the connection between integrated development planning and water demand planning. In conclusion, this study emphasises the emerging importance of the relationship between Integrated Development Planning and Water Demand Planning within the realm of public administration. Keywords: Integrated, Development, Planning, Water, Demand

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

Perencanaan Kebutuhan Air Pada Areal Irigasi Bendung Walahar Karawang, Jawa Barat


 Planning for water needs for irrigation areas is carried out to determine the water needs for plants to grow and develop. The source of water obtained for meeting water needs comes from the Walahar Dam. The results of water demand planning can also be used to simulate cropping patterns. Cropping patterns are very helpful for farmers in determining effective and efficient planting times. Calculation of plant water needs is processed from climatological data and rainfall data, the results of which are demand discharge. Based on the results of the calculation of water requirements for plants, the average discharge requirement is 0.65 m3/second – 9.59 m3/second, while the available water or available discharge in Walahar Weir is 183.90 m3/second. So it can be concluded that the available water in Walahar Weir can meet the water needs of plants.
 Keywords: Discharge, Climatological Data, Rainfall Data, Plant Water Demand Planning

Information Needs for Water Demand Planning and Management

AbstractA collaborative study that focused primarily on water utilities was conducted to determine barriers—and solutions—to effectively collecting, analyzing, and applying water use data.

Application of Watershed Distributed Water Quality Model for Prediction and Assessment in Nen River Basin

Water quality prediction is a tool to service for water environment management in strongly influence by human activities. In this paper, watershed distributed water quality model called WEQ is used to predict water quality, which is changed by pollutants from different sources. WEQ is a physical mechanism model and established the response-feedback relations between pollution emission on the land and water quality change in the river based on the theory of pollution generation and migration and transformation coupling with water cycle. Nen River Basin is the study area in this paper. Firstly predict social economic information, water demand and pollutants, then make some different water demand planning (general and strengthening of water saving) and pollution load prediction (basic and redline). Finally, use WEQ to predict the water quality and assessment the rate of water quality reaching standard of water function areas in the four scenarios. The simulation results show that QG-LB, QS-LB, QG-LR, QS-LR scenarios had 76%, 88%, 89% and 93% water function areas of reaching standard respectively, but different function areas have different situation about water quality. QS-LR scenario is a recommended plan for making policy.

Application of peak load for industrial water treatment plant design

Peak load rate(i.e., maximum daily flow/average daily flow) has not been considered for industrial water demand planning in Korea to date, while area unit method based on average daily flow has been applied to decide capacity of industrial water treatment plants(WTPs). Designers of industrial WTPs has assumed that peak load would not exist if operation rate of factories in industrial sites were close to 100%. However, peak load rates were calculated as 1.10~2.53 based on daily water flow from 2009 to 2014 for 9 industrial WTPs which have been operated more than 9 years(9-38 years). Furthermore, average operation rates of 9 industrial WTPs was less than 70% which means current area unit method has tendency to overestimate water demand. Therefore, it is not reasonable to consider peak load for the calculation of water demand under current area unit method application to prevent overestimation. However, for the precise future industrial water demand calculation more precise data gathering for average daily flow and consideration of peak load rate are recommended.

Intelligent autonomous system for residential water end use classification: Autoflow

Over half of the world's population will live in urban areas in the next decade, which will impose significant pressure on water security. The advanced management of water resources and their consumption is pivotal to maintaining a sustainable water future. To contribute to this goal, the aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water metres connected to dwellings includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed an intelligent application called Autoflow which served as a prototype tool to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events. However, this first prototype application achieved overall recognition accuracy of 85%, which is not sufficient for a commercial application. To improve this accuracy level, a larger dataset consisting of over 82,000 events from over 500 homes in Melbourne and South-east Queensland, Australia, were employed to derive a new single event recognition method employing a hybrid combination of Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and the Dynamic Time Warping (DTW) algorithm. The classified single event registry was then used as the foundations of a sophisticated hybrid ANN–HMM combined event disaggregation module, which was able to strip apart concurrently occurring end use events. The new hybrid model's recognition accuracy ranged from 85.9% to 96.1% for single events and 81.8–91.5% for combined event disaggregation, which was a 4.9% and 8.0% improvement, respectively, when compared to the first prototype model. The developed Autoflow tool has far-reaching implications for enhanced urban water demand planning and management, sustained customer behaviour change through more granular water conservation awareness, and better customer satisfaction with water utility providers.

ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran

The main goal of this study was to evaluate the different feed-forward back-propagation artificial neural networks’ (ANNs) potential to estimate and interpolate the reference crop evapotranspiration (ET0) in Fars province of Iran. ET0 was calculated using the FAO-56 Penman–Monteith method over 24 synoptic stations. Then, altitude, latitude, longitude and the month’s number as inputs and the monthly ET0 as output (target) were used to train the ANNs. In addition, the three-layered ANNs optimized with different training algorithms including gradient descent back-propagation (gd), gradient descent with adaptive learning rate back-propagation (gda), gradient descent with momentum and adaptive learning rate back-propagation (gdx) and scaled conjugate gradient back-propagation (scg). The results indicated that scg algorithm with architecture (4 2 1) had more satisfactory results with the RMSE and R correlation coefficient equal to 18.538 mm and 0.967 in validation phase, respectively. Based on the mentioned architecture of scg algorithm, and input data form different parts of Fars province and surrounding areas, monthly ET0 maps were produced and annual one achieved by summation of monthly maps. The maps particularly annual one showed that highest values of ET0 could be found in the southern and especially southeastern regions, while the lowest values of ET0 could be seen in the northern parts. Contribution of geographic and topographic variables improved the accuracy and spatial details of the resulting maps. It is interesting to note that the fundamental capability of this model is the usage of just a few parameters for ET0 mapping. Since ET0 is a key parameter in water demand planning, therefore, the derived maps could be useful and applicable for many purposes mainly irrigation scheduling in Fars province, Iran.

Analysis of Spatial Scenarios Aiding Decision Making for Regional Irrigation Water-Demand Planning

Regional irrigation water-demand planning is utilized to establish appropriate cropping patterns and estimate irrigation water demand. Although optimization methods have been extensively adopted, uncertainties of meteorological conditions and the complexity of spatial contexts make developing explicit and structured decision making extremely difficult. Rather than generating a single optimal solution, decision makers prefer to generate several possible scenarios and compare results. This study proposes a novel spatial scenario-based planning framework, with a database, model base, and scenario-setting modules, to generate flexible spatial planning scenarios for improving irrigation water-demand planning. Possible demand planning scenarios for irrigation managers are discussed. A prototype of the proposed scenario-based framework is implemented on a geographic information system platform to assist in spatial decision making. Demand planning during a drought period for the Chia-Nan irrigation command area, the largest one in Taiwan, is adopted as a case study to demonstrate the proposed framework for spatial scenario analysis.

Simple weather‐water demand model

Abstract To employ plain simulation instead of complicated correlation consumption models one has to utilize modern database facilities. Reciprocally, theoretical rules have to be built into the database as a criterion for the data import into this database and for debugging of previously imported data. Practical use of the model is very forthright; no special program is required. Its applications are: analysis of pricing policies impact, water demand planning, simulation of water demand for new cities and suburbs, etc.