Unconventional Water Use Allocation in Harbin, China

Abstract

To lessen the strain on Harbin’s water resources and enhance the environment, it is crucial to analyze the key determining factors for the use of unconventional water resources in the city and to allocate unconventional water reasonably among various industries in the planning year. In this study, a back-propagation neural network (BP neural network) model is used to predict the potential for unconventional water resource utilization in the planning year (2025), a gray correlation analysis model is used to evaluate water-using industries, and finally, an unconventional water resource allocation scheme for the study is used to determine the main influencing factors and determine the weights of key indicators. The findings demonstrate a strong correlation between Harbin’s level of investment and construction, economic efficiency, cost, level of water demand, and social factors, as well as a low level of utilization of unconventional water resources throughout the city.

1. Introduction

Nowadays, water shortage, spatial inequality, and the lack of sustainable water resources are current global issues that have a significant impact on people’s everyday activities and quality of life [1]. The pressure of water resources supply and demand can be successfully reduced through integrating unconventional water resources into a unified water resource scheduling system using conventional water resources and unconventional water resource allocation in concert [2]. Currently, the treatment and utilization of unconventional water resources are at the forefront due to the efforts of countries such as India, the United States, Australia, and Japan [3]. As the first country in the world to analyze the health and environmental risks associated with sewage recycling, Australian relevant rules and laws are more comprehensive. In particular, the California state government has specific norms and strict supervision over the quality of sewage water, wastewater treatment technology, the use of different grades of reclaimed water, reclaimed water plant equipment, reclaimed water transportation system networks, and the use of the terminal. The United States is among the world’s top countries that use unconventional water resources and allocates much scientific research to this use, and California is the leader among the US states. Despite China’s abundant total water resources, its per capita water availability is significantly low due to its dense population. China will continue to be plagued by the disparity between water resource supply and demand in the coming decades [4]. China is a country with a severe shortage of freshwater resources, which only make up about 10% of the global total. This represents a shortage of resources for China’s socio-economic development, which has become one of the key factors limiting China’s overall development, according to the Global Environment Outlook (GEO) document published by the United Nations Environment Program [5]. As a result, the expansion of unconventional water resource utilization and its scientific and reasonable allocation are pivotal tasks currently undertaken in China [6,7,8].

All water sources excluding surface water and groundwater, which are considered conventional water sources, are referred to as “unconventional water resources”. This refers to recycled water, rainwater collection, desalinated seawater, brackish water, pit water, etc., which can be used after treatment or can be used directly under certain circumstances. Unconventional water resources can be a better alternative to traditional water resources in the fields of industry, municipal affairs, agricultural irrigation, etc., and they play a significant role in reducing the shortage of water resources in cities and addressing the problem of water pollution [9]. Rainwater, domestic sewage, industrial wastewater, mine pit water, brackish water, etc. are typically collected and transported to sewage treatment plants for unconventional water resource utilization. After a series of processes such as sedimentation, filtration, biochemical reaction to eliminate harmful pollutants, and ultraviolet ray poisoning, they are discharged after water quality tests meet the national discharge standard [10]. Reclaimed water and rainwater currently constitute the primary portion of Harbin City’s unconventional water resources. The first requirement for allocating unconventional water resources in various regions and industries is ensuring that recycled water meets water quality standards. In this study, we analyzed Harbin’s unconventional water resources using the following parameters: BOD₅, fecal coliform, anionic surfactants, and suspended solids. The results of the water quality testing of unconventional water show that the concentration of BOD₅, fecal coliform, and anionic surfactants is higher than that of suspended solids. The findings demonstrate that each index’s concentration in unconventional water complies with Chinese water sanitation standards. A flowchart showing the allocation of unconventional water resources in Harbin City is depicted below (as shown in Figure 1).

It is important to determine a reasonable evaluation index system, unconventional water resources in different regions, and different water industries to carry out scientific and reasonable allocation. Ensuring that the water quality meets the emission standards and industry water quality requirements is only the first step in the allocation of unconventional water resources utilization [11]. Li used Spearman correlation and redundancy analyses to determine the main influencing factors on the utilization level of unconventional water resources, and he chose environmental factors and water quality as the influencing factors. He came to the conclusion that reclaimed water, as a source of recharge, has a great potential to create a new ecosystem in the river [12]. To develop a sound evaluation method based on rough set theory for the evaluation of unconventional water resources, Wu identified water resources endowment conditions and conventional water resources utilization as the driving factors; water conveyance engineering conditions, economic conditions, ecological conditions, etc. as the constraints; and health conditions as the risk factors. In order to create a rough set theory based on the determination of the weights of the evaluation, for the evaluation of unconventional water resources to provide a good evaluation index system, driving factors, water conveyance engineering conditions, economic conditions, ecological conditions, etc. [9]; therefore, when assessing the significant factors influencing the utilization of unconventional water resources, indicators such as water resource status, water consumption level, urban development planning, and economic indexes should be taken into account. This is especially true for Chinese cities, where the allocation of water resources is frequently related to local urban development planning and water resources [13,14]. Li Jun created the interregional hierarchical planning model (INHPM) for a study on the utilization of unconventional water resources and future directions in order to guide the conventional and unconventional water supply sectors toward coordinated development through the objective function of the degree of synergy [15]. In his research on the allocation of unconventional water resources, Yang Zhe created an integrated stochastic MCDM framework that uses both quantitative and qualitative criteria to solve challenges involving the use of unusual water resources [16]. Through establishing a system of evaluation indicators using the “Sandoval-Solis” and “Multivariate Copula” methods, Yazdandoost Farhad calculated the extent of utilization and relative sustainability probability of unconventional water resources as an alternative to conventional water resources in the Iranian region [17]. Gray water, as an unusual water resource, is sufficient to provide a sustainable future, according to quantitative analysis of gray water resources in Egypt by Batisha Ayman F [18]. Negm Abdelazim M found appropriate evaluation indicators to examine and assess the usage of unconventional water resources for irrigated agriculture in Egypt. [19]. Through SWOT analysis, Karimov B. K. demonstrated that unconventional water resources are better suited for the establishment of intensive aquaculture–agriculture systems [20]. A method for estimating the amount of unconventional water resources used for domestic and commercial purposes was presented by Xiang Yang Jiang [21]. Due to the current state of the domestic and international agricultural use of unconventional water resources, it is more widely used in agriculture. In order to increase the level of utilization of unconventional water resources, standards and norms should be improved, risk management should be strengthened, systematic research should be conducted, and other factors should be considered [22]. According to the current state of China’s agricultural use of unconventional water resources, some studies have summarized the irrigation method for such resources and proposed six safety precautions for their safe irrigation, including irrigation zoning technology, pollution identification technology, and high-efficiency irrigation technology, and proposed development strategies for enhancing irrigation technology research and promotion, perfecting the standard and normative system for the reuse of unconventional water resources in agriculture, and formulating incentive policies for the exploitation and utilization of unconventional water resources in agriculture, in order to set up a technology system of utilization of unconventional water resources [23]. Liu Yu studied Tianjin’s current situation of unconventional water use and, through the analysis of water resource utilization pathways and water estimation, came to the conclusion that the amount of rainwater and renewable water resources available can fully satisfy the suitable ecological water demand of the coastal estuary. She then proposed measures for ecological water replenishment of the coastal estuary [24]. Gao Chuanchang examined how reclaimed water, rainwater, seawater, and brackish water are currently used domestically and internationally and offered recommendations for China’s unconventional water resource use [25]. Li Jun looked into the synergistic allocation model of conventional and unconventional water resources, which takes into account the interests of various decision makers, based on the interests of decision makers at different levels. Regional managers and water suppliers of conventional and unconventional water resources form the upper and lower layers of a two-layer water allocation model, which is then solved using the NSGAIII-NSGAII multi-objective algorithms while accounting for the decision space of the decision makers. On this foundation, the TOPSIS approach is used to maximize the best outcomes for both this model’s and the traditional model under various scenarios. Shaanxi Province’s Yulin City’s Yuyang District served as the research subject for this model’s validation [26]. In Mu Ying’s research, she delved into three noteworthy examples of unconventional water sources and offered solutions and countermeasures for the issues China is currently facing [27].

Previous research has revealed that selecting an appropriate indicator layer is necessary for the distribution of unusual water resources. In Harbin, it is calculated through selecting the influencing factors of the level of unconventional water resources utilization based on existing studies. In this study, we chose the most significant correlation among many factors as the evaluation index, predicting the potential of unconventional water resources utilization in the planning year based on the level of unconventional water utilization year by year, and calculated the allocation ratio of each industry using a reasonable indicator. Principal component analysis (PCA) and Spearman correlation analysis can be used to determine indicators and their weights, which can prevent subjective considerations from interfering with scientific allocation [28], and a BP neural network forecasts Harbin City’s capacity to utilize unconventional water resources in the planning year (2025). The planned year is determined using gray correlation analysis. We thus present a program for allocating unconventional water resources [29,30,31,32,33].

2. Study Area

Harbin is a member of the Songhua River system and may be found in Heilongjiang Province’s south central region. Compared to other provincial capital cities, Harbin has more abundant water resources (Figure 2). There are 197 reservoirs in the city, including the Hongxing Reservoir and the Xiquanyuan Reservoir, in addition to the 466 km of the main stream of the Songhua River inside the Harbin region and the 2021 km length of 22 important tributaries, including the Larin River and the Ash River. According to the data, Harbin has average rainfall, average groundwater resources, and average surface water resources of 619.11 mm, 3 billion m³, and 9.92 billion m³, respectively. However, in recent years, rainfall has decreased and surface water evaporation has increased due to regional climate change, excessive groundwater use, resource waste, an increase in pollution levels, and a decrease in locally produced water. Transit water is more plentiful, and there are other significant issues with the uneven spatial distribution of water supplies [34,35].

Both the county and the city of Harbin should have sewage treatment rates of 95% and 85%, respectively. Harbin City has 15 wastewater treatment facilities that treat wastewater at a rate of 95.47 percent as of 2021. Wenchang, Taiping, Hejiagou, Songbei Jile, Xinyigou, Bayan County, Mulan County, Tonghe County, Bin County, Yilan County, and Fangzheng County all have wastewater treatment facilities that are now operational. Reclaimed water must be included in the coordinated distribution of water resources because it is the primary supplemental water source in Harbin City.

3. Methodology

3.1. Observation of the Influences

In addition to a prior study on the social variables (A), the allocation of unconventional water resources is influenced by the economy, humanities, politics, and other factors. These elements include the number of people, the proportion of people living in urban areas, and the urban population [36]. The choice of including gross regional product, cost, and the economic efficiency of the sum of the two indicators is made for the economic structure factors (B) [37]; per capita water use, the volume of surface water, the quantity of groundwater used, and 9 other indicators were chosen to be included in the water use structure category (C) [38]; infrastructure investment, the building of recycled water facilities, and three other indicators were picked for the list of financial investment influencing determinants (D) [39] (as shown in Table 1).

3.2. Identifying Key Indicators

Because each indicator has a different level of influence, this study uses the PCA method [40,41] and the Spearman algorithm [42] to select the features of complex parameters and screen out the important parameters that have the greatest impact on the amount of unconventional water use in Harbin. Principal component analysis (PCA) is a widely used data dimensionality reduction algorithm that determines the significant influence of the correlation coefficient of the factors. Its main idea is to map the n-dimensional features to the k-dimension, which is a new orthogonal feature also known as the principal component; when two-column variables are involved, Spearman is mostly employed to address the issue of correlation between the goal data and the variable data. In this study, the 17 influencing factors were investigated using Spearman correlation analysis with the target data (unconventional water consumption), and the correlation coefficients between each factor and the target data were ultimately determined. The weights of the key indicators are then determined using the correlation coefficients of the key indicators that have been chosen.

(1)$\left\{\begin{array}{l}{X}^{\prime }={(X_{ij})}_{n\times p}=(X_1{}^{\prime },X_2{}^{\prime }\dots ,X_p{}^{\prime })\\ M_{ij}=\frac{(X_{ij}-{\overline{X}}_j)}{S_j},i=1,2\dots ,n;j=1,2\dots ,p\\ {\overline{X}}_j={\displaystyle \sum _{i=1}^n\frac{X_{ij}}{n},S_j{}^2={\displaystyle \sum _{i=1}^n\frac{{(X_{ij}-{\overline{X}}_j)}^2}{n-1}}}\\ M={(M_{ij})}_{n\times p}=(M_1,M_2\dots ,M_p)\end{array}\right.$

In Equation (1), (X_ij)_n×p denotes n sets of data with p-dimensional variables in each set, ${\overline{X}}_j$ denotes the mean of the jth variable, and S_j is the standard deviation of the jth variable.

(2)$R^{″}=\frac{1}{n-1}{M}^{T}M$

In Equation (2), R″ denotes an n × n-dimensional symmetric matrix with all diagonals being 1.

(3)$\left|\lambda I-R^{″}\right|=0$

In Equation (3), “I” is the unit matrix. After ${\lambda }_i$ is found, it is sorted by size.

(4)${\lambda }_1\ge {\lambda }_2\ge \dots {\lambda }_p\ge 0$

(5)$M{X}_i{}^{\prime }={(M{X}_{i1}{}^{\prime },M{X}_{i2}{}^{\prime },\dots ,M{X}_i{{}_p}^{\prime })}^{\prime }$

In Equation (5), MXi′ is the feature vector.

(6)$\left\{\begin{array}{l}{\alpha }_{1i}={\lambda }_i/{\displaystyle \sum _{i=1}^p{\lambda }_i}\\ C_1{T}_i={\displaystyle \sum _{i=1}^m{\lambda }_i/}{\displaystyle \sum _{i=1}^p{\lambda }_i}\end{array}\right.$

In Equation (6), ${\alpha }_{1i}$ indicates the contribution of the principal component; C₁T_i denotes the contribution of principal component ${\alpha }_{1i}$ with the cumulative contribution of the previous m principal components.

Through the above steps, the key indicators that have a significant impact on the level of unconventional water resources utilization in Harbin have been derived, and then the correlation coefficients of the key indicators have been calculated using the Spearman method. Bivariate correlation analysis was performed for each set of variables with Harbin’s unconventional water resources utilization, which were set as two n-dimensional vectors, X, Y, and the expressions were $\left[X_1,X_2,\dots ,X_{\mathrm{n}}\right],\left[Y_1,Y_2,\dots ,Y_{\mathrm{n}}\right]$. The elements of the vector are sorted using the expression $\left[x_1,x_2,\dots ,x_n\right],\left[y_1,y_2,\dots ,y_n\right]$.

(7)${\rho }_{(x,y)}=\frac{{\displaystyle \sum _{i=1}^n(x_i-\overline{x})(x_i-\overline{y})}}{\sqrt{{\displaystyle \sum _{i=1}^n(x_i-\overline{x}{)}^{{}^2}{\displaystyle \sum _{i=1}^n(x_i-\overline{y}{)}^{{}^2}}}}}$

In Equation (7), $\rho (x,y)$ denotes the correlation coefficient.

(8)${\omega }_i=\frac{{\rho }_i}{{\displaystyle \sum _{i=1}^n{\rho }_i}}$

In Equation (8), ${\omega }_i$ indicates the weight of each indicator.

3.3. Forecasting the Potential for Unconventional Water Use

The creation and use of unconventional water resources are crucial for reducing the disparity between water supply and demand and for enhancing the quality of aquatic ecosystems [43]. Since the “three living waters” theory was proposed, water resource allocation has tended to favor the three facets of production, ecology, and life; hence, unconventional water resources are also exploited in agricultural irrigation, industry, residential life, and ecological environment replenishment. Currently, we gather data from the Harbin indicators and use a BP neural network to forecast the potential for unconventional water resource use (UWRU) in the planned year (2025). A multi-layer feed-forward neural network with excellent learning efficiency, convergence, and generalization capability is known as a BP neural network. It is trained using the error back-propagation technique. The following diagram (Figure 3) illustrates the BP neural network prediction model’s flow [44].

In this study, five implicit layers are set up, with the output layer y being the unconventional water resource utilization (UWRU), which is programmed and solved using Matlab. The input layers are the level of investment and construction (x₁), economic benefits (x₂), costs (x₃), water demand (x₄), and social factors (x₅). The following are the mathematical expressions for the BP neural network’s prediction process.

(9)${\stackrel{⌢}{X}}_i=\frac{X_i-X_{\mathit{min}}}{X_{\mathit{max}}-X_{\mathit{min}}}$

(10)$y=({\displaystyle \sum _{i=1}^5{\displaystyle \sum _{j=1}^5{\omega }_{1j}^{(5,5)}\times tansig({\displaystyle \sum _{i=1}^5({\omega }_{1i}^{(5,1)}{x}_i}}})+b_j^{(5)}))+b_1^{(5)}$

In Equation (9), “X_i” denotes the current value of each factor; “X_max” and “X_min” denote the maximum and minimum values of each factor, respectively.

In Equation (10), ${\omega }_{1j}^{(5,5)}$ denotes the weight from the hidden layer to the output layer; ${\omega }_{1i}^{(5,1)}$ denotes the weight from the input layer to the hidden layer; “b_j⁽⁵⁾” denotes the bias term of the hidden layer node.

3.4. Modeling of Unconventional Water Allocation

The next step is to use gray relation analysis (GRA), a mathematical technique to measure the correlation between two vectors based on the distance between the vectors, to allocate unconventional water resources to various sectors now that the potential of unconventional water resource utilization in Harbin city has been predicted for the planning year. It is possible to weight the distribution of unconventional water resources to different sectors based on the “degree of correlation” using GRA, a mathematical measure of the distance between two vectors.

Construct a matrix based on indicator data.

(11)${X_{\mathrm{ik}}}^{\prime }=\left(\begin{array}{l}{X_{1(1)}}^{\prime }{X_{2(1)}}^{\prime }{X_{3(1)}}^{\prime }{X_{4(1)}}^{\prime }\\ {X_{1(2)}}^{\prime }{X_{2(2)}}^{\prime }{X_{3(2)}}^{\prime }{X_{4(2)}}^{\prime }\\ {X_{1(3)}}^{\prime }{X_{2(3)}}^{\prime }{X_{3(3)}}^{\prime }{X_{4(3)}}^{\prime }\\ {X_{1(4)}}^{\prime }{X_{2(4)}}^{\prime }{X_{3(4)}}^{\prime }{X_{4(4)}}^{\prime }\\ {X_{1(5)}}^{\prime }{X_{2(5)}}^{\prime }{X_{3(5)}}^{\prime }{X_{4(5)}}^{\prime }\end{array}\right)$

where “i” denotes water use structure and “k” denotes evaluation index.

The index data were dimensionless using the mean value method.

(12)$X_{i\left(k\right)}=\frac{x_{i\left(k\right)}{}^{\prime }}{\frac{1}{4}{\displaystyle \sum _{k=1}^4{x}_{i\left(k\right)}{}^{\prime }}}$

In Equation (12), “$X_{i \left(k\right)}$′” indicates the value of each water use item in each index.

Determining the reference series: The reference series should be an ideal standard of comparison, and in this study the optimal value is selected to constitute the reference series after normalizing each index.

(13)X₀′ = (X_n1max, X_n2max, X_n3max, X_n4max, X_n5max)

Calculate the absolute difference between the index sequence of each evaluated object and the corresponding element of the reference sequence one by one.

(14)$D=\left|X_{0(k)}-X_{i(k)}\right|$

In Equation (14), k = 1, 2, 3, 4, 5; i = 1, 2, 3, 4.

Extremely poor calculation:

(15)$D_{\mathit{max}}=\underset{i=1}{\overset{4}{\mathit{max}}} \underset{k=1}{\overset{5}{\mathit{max}}}\left|X_{0(k)}{-X}_{i\left(k\right)}\right|$

(16)$D_{\mathit{min}}=\underset{i=1}{\overset{4}{\mathit{min}}} \underset{k=1}{\overset{5}{\mathit{min}}}\left|X_{0(k)}{-X}_{i\left(k\right)}\right|$

Calculate the number of correlation coefficients of the corresponding elements of each comparison sequence separately.

(17)${\zeta }_{i\left(k\right)}=\frac{\underset{i\begin{array}{ccc}& & k\end{array}}{\overset{}{\mathit{min} \mathit{min}}}\left|X_{0\left(k\right)}{-X}_{i(k)}\right|+\rho \underset{i\begin{array}{ccc}& & k\end{array}}{\overset{}{\mathit{max}\mathit{max}}}\left|X_{0\left(k\right)}{-X}_{i(k)}\right|}{\left|X_{0\left(k\right)}{-X}_{i(k)}\right|+\rho \underset{i\begin{array}{ccc}& & k\end{array}}{\mathit{max}\mathit{max}}\left|X_{0\left(k\right)}{-X}_{i(k)}\right|}$

In Equation (17), $\rho$ is the resolution factor; the larger the difference between the correlation coefficients, the stronger the differentiation ability, and usually $\rho$ is taken as 0.5.

If $\left\{X_{0\left(k\right)}\right\}$ is the best value data column, the larger ${\zeta }_{i\left(k\right)}$ is better.

If $\left\{X_{0\left(k\right)}\right\}$ is the worst value data column, the smaller ${\zeta }_{i\left(k\right)}$ is better.

In this study, the optimal value data columns are used, so it is necessary to calculate the correlation coefficients of the five sets of series with the reference data columns.

Calculation of correlation coefficients: Since each indicator plays a different role in the comprehensive evaluation in this study, the weighted averages of the correlation coefficients were used [45].

(18)${\gamma }_{oi}^{\prime }=\frac{1}{m}{\displaystyle \sum _{k=1}^m{\omega }_k}\cdot {\zeta }_{i\left(k\right)}$

where ${\omega }_{\mathrm{k}}$ is the weight of each indicator.

Based on the ranking of the correlation of each observation, a comprehensive configuration result can be obtained.

4. Results

4.1. Situational Setting

Harbin is a member of the Songhua River water system and is situated in the southernmost region of Heilongjiang Province. Water resources are more abundant in Harbin than in other provincial capitals. Harbin’s domain includes the Songhua River’s main channel, which is about 466 km long, as well as 22 significant tributaries, including the Lalin River and the Ash River, which together have a length of nearly 2021 km. The city also has 197 reservoirs, including the Hongxing Reservoir and the Xiquanye Reservoir [46]. Unconventional water sources are currently mostly employed in Harbin to replenish rivers and lakes. In this study, the allocation of unconventional water resources in four water projects—agriculture, industry, residential life, and ecological water recharge—is taken into account. These factors are the investment and construction level (x₁), economic benefit (x₂), cost (x₃), water demand level (x₄), and social factors (x₅). By the projection year (2025), each water use industry will have allocated conventional and unconventional water resources in a synergistic ratio of 50% and 75% of the hydrological year (2025).

4.2. Determining Evaluation Indicators and Weights

The correlation coefficients of the influencing factors were calculated via PCA and Spearman’s method (as shown in Table 2, Figure 4).

From the above results, it can be concluded that the level of unconventional water resources utilization in Harbin City has high correlation with the level of investment and construction (x₁), economic benefits (x₂), cost (x₃), level of water demand (x₄), and social factors (x₅). Calculate the weight of each indicator according to Equation (7) (as shown in Table 3).

4.3. Annual Levels of Anticipated Unconventional Water Use

The level of unconventional water use in Harbin City in the forecast year (2025) was predicted using the BP neural network. This information, along with the water quotas for each industry in the 50% and 75% hydrological years, allowed for the prediction of the water demand for agricultural irrigation (IW), industry (IC), domestic (DWC), and ecological (EWS) purposes in 2025. Figure 5 illustrates the BP neural network fitting procedure for the potential use of unconventional water resources.

As can be seen from Figure 5, the BP neural network fitting process is good, and the error between the fitted value and the actual value is small. According to the running code Harbin’s unconventional water resources utilization potential prediction formula can be derived:

$\begin{array}{ll}y=& -1.2138\times \mathit{tan}\mathit{sig}(-0.7725\times X_1-2.9847\times X_2+0.0423\times X_3-1.5782\times X_4+0.4989\times X_5+2.4019)\hfill \\ & +0.0937\times \mathit{tan}\mathit{sig}(0.4106\times X_1-0.3985\times X_2-0.1242\times X_3+0.5368\times X_4-1.8723\times X_5+1.8570)\hfill \\ & +0.0283\times \mathit{tan}\mathit{sig}(0.8456\times X_1-1.6127\times X_2-0.7766\times X_3-0.2167\times X_4+0.6873 \times X_5+0.1232)\hfill \\ & -0.1445\times \mathit{tan}\mathit{sig}(1.2978\times X_1+0.5708\times X_2+0.6669\times X_3+1.0779\times X_4+1.5866\times X_5+0.5890)\hfill \\ & +0.5469\times \mathit{tan}\mathit{sig}(0.2623\times X_1-0.6794\times X_2-0.4032\times X_3+1.1548\times X_4-1.3356\times X_5+1.8738)\hfill \\ & -0.5568\hfill \end{array}$

When the independent variables are substituted into the formula, the result is that Harbin City will use 0.99 × 10⁸ m³ of unconventional water resources in the projected year (2025). The 50% hydrological annual water demand for agricultural irrigation is 49.63 × 10⁸ m³, 0.06 × 10⁸ m³ for industry, 4.51 × 10⁸ m³ for residential, and 1.38 × 10⁸ m³ for ecological recharge; the 75% hydrological annual water demand for agricultural irrigation is 54.86 × 10⁸ m³, 0.07 × 10⁸ m³ for industry, 4.98 × 10⁸ m³ for residential, and 1.53 × 10⁸ m³ for ecological recharge.

4.4. Options for Unconventional Water Allocation in Various Situations

When the forecast year is 50% of the hydrological year, the total water demand in Harbin is 55.58 billion m³. When the forecast year is 75% of the hydrological year, the total water demand in Harbin is 61.44 × 10⁸ m³. The amount of unconventional water resources available in 2025 is predicted to be 0.99 × 10⁸ m³. According to Equations (14)–(21), the allocation ratio of unconventional water resources in the four water use projects of agriculture (AGR), industry (IND), residential life (LIF), and ecological water replenishment (ZOO) is calculated, as shown in Figure 4. The design of each industry gives priority to the use of unconventional water, and when the amount of unconventional water resources cannot meet the demand of the industry, the industry’s water demand is replenished with clear water. Therefore, when the forecast year is 50% of the hydrological year, the unconventional water use for agricultural irrigation is 0.34 × 10⁸ m³ and the conventional water use is 49.29 × 10⁸ m³; the industrial unconventional water use is 0.06 × 10⁸ m³ and the conventional water use is 0.0 × 10⁸ m³; the unconventional water consumption for residential life is 0.31 × 10⁸ m³ and the conventional water consumption is 4.21 × 10⁸ m³; the unconventional water consumption of ecological recharge is 0.28 × 10⁸ m³ and the conventional water consumption is 1.10 × 10⁸ m³. When the forecast year is 75% of the hydrological year, the unconventional water use for agricultural irrigation is 0.34 × 10⁸ m³ and the conventional water use is 54.52 × 10⁸ m³; the industrial unconventional water use is 0.07 × 10⁸ m³ and the conventional water use is 0.0 × 10⁸ m³; the unconventional water consumption for residential life is 0.03 × 10⁸ m³ and the conventional water consumption is 4.68 × 10⁸ m³; the unconventional water use for ecological recharge is 0.28 × 10⁸ m³ and the conventional water use is 1.25 × 10⁸ m³ (as shown in Figure 6).

4.5. Unusual Water Space Arrangements in Each County

Harbin can be divided into 13 counties, namely, the main urban area, Acheng District, Hulan District, Shuangcheng District, Wuchang City, Shangzhi City, Bin County, Founder County, Yanshou County, Bayan County, Mulan County, Tonghe County, and Yilan County, each of which has its own characteristics in terms of agricultural irrigation (IW), industrial water use (IC), domestic water use (DWC), ecological water recharge (EWS), and total water use (TWC) (as shown in Figure 7).

From the above calculation results, the amount of unconventional water resources available in Harbin in the planning year (2025) is 0.99 × 10⁸ m³. The spatial allocation of unconventional water is now based on the water demand of each county and district and the four water use sectors of each county in agricultural irrigation (IW), industrial water use (IC), domestic water use (DWC), and ecological water recharge (EWS) (as shown in Figure 8).

5. Discussion

• (1). Every nation is experiencing a number of critical issues with water resources at the moment, including severe water shortages and water contamination. The best method to address these issues is to actively develop and use unconventional water sources. The Middle Eastern countries are rich in oil resources and can develop and use mine water, as well as the United States, Germany, France, and other countries with more developed industries. China, India, and the United States have a large population, so industrial wastewater and domestic sewage discharge is extremely large, though it can be recycled after the treatment of sewage, increasing the use of recycled water configuration. Japan, South Africa, and other countries should be tailored to local conditions. In addition to gathering, storing, and analyzing digital data and information regarding the use and configuration of unconventional water resources, countries should promote international cooperation in the treatment and utilization of unconventional water resources. Study the socioeconomic potential and ecological worth of unconventional water allocation while developing and implementing innovative technology for the treatment of unconventional water resources.

• (2). To implement the “water conservation first” strategy to water management in this new era, China is gradually focusing on the use of unconventional water resources, replacing traditional water resources with unconventional water resources in many industries and encouraging people and businesses to use unconventional water. This is a crucial step in putting the new “water conservation first” water management strategy into practice. This is a crucial step in putting the new era’s “water conservation first” strategy into practice. Based on the state of China’s growth, it is a crucial step to actualize the creation of an “environmentally friendly and resource-saving society” and to contribute to the country’s healthy and high-quality development. It is a fantastic activity founded on the shared ideals of the entire civilization, the entire planet, and the entire human race, and it is extremely important for creating a community of human destiny and influencing the development of the common home of mankind in a better way.

• (3). Harbin City has a fair amount of overall water resources, but it also has a number of issues, including major water pollution and water shortages related to water quality. In Harbin, there are 11 sewage treatment facilities with a daily capacity of 1.4 × 10⁶ t. A portion of the water industry can essentially satisfy its needs in terms of quantity and quality of water, but the majority of it is simply dumped into lakes and rivers without being properly utilized. As a result, Harbin has to set up a comprehensive pipeline network, enhance funding for the development of facilities for treating and allocating unusual water resources, and create a plan for allocating those resources that is both rational and based on science.

6. Conclusions

The pressure of water supply and demand in Harbin can be reduced by a significant amount via the rational distribution of unconventional water resources. However, the majority of recent research on the allocation of unconventional water resources focuses on the water resource demands of each region and sector to establish the allocation program, omitting the importance of the economy, way of life, and other aspects of the impact. This study uses Harbin as the study area based on prior research, identifies the indicators that have a greater impact on the amount of unconventional water resources utilized in Harbin, forecasts the potential for unconventional water resources utilization in the planning year, and then, based on the screened indicators, allocates unconventional water resources reasonably to four water-using industries in 13 counties and districts of Harbin.

• (1). According to the screened indicators of the impact on the level of unconventional water resources utilization in Harbin, actions like increasing capital investment in unconventional water resources in Harbin, constructing more sewage treatment plants, expanding sewage treatment plants, upgrading the water pipeline network system, and stepping up the publicity of unconventional water resources can effectively improve the level of unconventional water resources.

• (2). The results of the forecast indicate that 0.99 × 10⁸ m³ of unconventional water resources will be used in the Harbin planning year (2025); however, this amount is negligible in comparison to the level of water demand. The amount of unconventional water resources available in Harbin is insufficient to cover the city’s water needs, so it is anticipated that each industry that uses water will give unconventional water use priority while still meeting the rest of the city’s water needs with conventional water sources.

• (3). Agricultural irrigation is given the highest priority for unconventional water resources in industrial water allocation, whereas industrial water is given the lowest priority. Priority levels for the 13 counties and districts are as follows: Wuchang City > Tonghe County > Main City > Yanshou County > Mulan County > Shangzhi City > Fangzheng County > Yilan County > Bayan County > Shuangcheng District > Acheng District > Hulan District > Bingxian County.

Author Contributions

H.G. contributed to conceptualization, methodology, software, formal analysis, investigation, writing—original draft, writing—review and editing, and visualization. Y.S. contributed to writing—review and editing and funding acquisition. Y.T. contributed to writing—review and editing, resources, and funding acquisition. H.D. contributed to investigation, supervision, and resources. H.L. contributed to project administration and investigation. L.W. contributed to writing—review and editing and resources. Z.W. contributed to the investigation. J.Y. contributed to the investigation. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

All data and materials support the published claims and comply with field standards.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. A flowchart showing unconventional water resource utilization.

Figure 2. Harbin overview map.

Figure 3. Schematic diagram of BP neural network prediction.

Figure 4. Correlation of each influencing factor.

Figure 5. Fitting process of unconventional water resources utilization potential in Harbin. (The red line in the graph shows the true value, and the blue line shows the predicted value).

Figure 6. Conventional and unconventional water use by water sector for the 50% and 75% hydrological year scenarios (CWC indicates conventional water use and UWC indicates unconventional water use).

Figure 7. Water use by county in Harbin.

Figure 7. Water use by county in Harbin.

Figure 8. Unconventional water allocation by county.

Figure 8. Unconventional water allocation by county.

Figure 8. Unconventional water allocation by county.

Table 1

Influencing factors.

Tier 1 Indicators	Secondary Indicators	Tier 1 Indicators	Secondary Indicators
Social factors (A)	Population size (PS)	Water use structure factors (C)	Total water resources (WRA)
	Urban population (UP)		Surface water volume (SWR)
	Percentage of urban population (POUP)		Groundwater volume (QOGR)
Economic structural factors (B)	Cost (COST)		Precipitation (PRE)
	Benefits (BENEFIT)		Total water supply (WS)
	Financial Expenditures (FE)		Water consumption for agricultural irrigation (IW)
Financial input influence factors (D)	Reclaimed water facilities investment construction (REFIAC)		Industrial water consumption (IC)
	Number of sewage treatment plants (STP)		Domestic water consumption (DWC)
			Ecological water replenishment (EWS)

Table 2

Correlation of each indicator.

Influencing Factors	Relevance	Sig
Population size (PS)	0.129	0.740
Urban population (UP)	−0.159	0.682
Percentage of urban population (POUP)	0.837	0.005
Cost (COST)	0.653	0.057
Benefits (BENEFIT)	−0.398	0.288
Financial Expenditures (FE)	0.807	0.009
Total water resources (WRA)	0.448	0.226
Surface water volume (SWR)	0.339	0.373
Groundwater volume (QOGR)	0.667	0.050
Precipitation (PRE)	0.448	0.226
Total water supply (WS)	−0.588	0.096
Water consumption for agricultural irrigation (IW)	−0.588	0.096
Industrial water consumption (IC)	−0.837	0.005
Domestic water consumption (DWC)	0.530	0.142
Ecological water replenishment (EWS)	0.837	0.005
Reclaimed water facilities investment construction (REFIAC)	0.837	0.005
Number of sewage treatment plants (STP)	0.705	0.034

Table 3

Evaluation indicators and weights.

Evaluation Indicators	Weights (${\mathit{\omega }}_{\mathit{i}}$)
Investment and construction level (x1)	0.288
Economic benefits (x2)	0.147
Cost (x3)	0.240
Water demand level (x4)	0.187
Social factors (x5)	0.138

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