Agricultural Water Resources Management Using Maximum Entropy and Entropy-Weight-Based TOPSIS Methods

Abstract

Allocation and management of agricultural water resources is an emerging concern due to diminishing water supplies and increasing water demands. To achieve economic, social, and environmental goals in a specific irrigation district, decisions should be made subject to the changing water supply and water demand—the two critical random parameters in agricultural water resources management. This paper presents the foundations of a systematic framework for agricultural water resources management, including determination of distribution functions, joint probability of water supply and water demand, optimal allocation of agricultural water resources, and evaluation of various schemes according to agricultural water resources carrying capacity. The maximum entropy method is used to estimate parameters of probability distributions of water supply and demand, which is the basic for the other parts of the framework. The entropy-weight-based TOPSIS method is applied to evaluate agricultural water resources allocation schemes, because it avoids the subjectivity of weight determination and reflects the dynamic changing trend of agricultural water resources carrying capacity. A case study using an irrigation district in Northeast China is used to demonstrate the feasibility and applicability of the framework. It is found that the framework works effectively to balance multiple objectives and provides alternative schemes, considering the combinatorial variety of water supply and water demand, which are conducive to agricultural water resources planning.

Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination

“…The first test scenario was developed to validate the performance of the Expert MPC, as well as its rejection to the negative impact of the plant time-varying parameters near specified nominal operating conditions. The design parameters of the Expert MPC were next: The sampling period was established at T = 10 s, the minimum and maximum prediction horizons were set like N 1 = 3, N 2 = 15 samples respectively, the control horizon was established as N u = 3, and the positive definite weighting matrices were set like R = diag(5, 1) and Q = diag (1,6), respectively. Figure 6 shows, in the PanelView 1500 of the pilot-scale plant, the real-time closed-loop responses of the control system with the designed Expert MPC.…”

“…Water scarcity has become a progressively growing problem worldwide due to the accelerated increase in water demand, which is expanding nowadays at a rate never seen before in any previous time [1][2][3][4][5]. Therefore, the effective management of water resources is a challenge to face the complexity of the real problem [6][7][8][9].…”

Temporal and Spatial Evolution and Obstacle Diagnosis of Resource and Environment Carrying Capacity in the Loess Plateau

“…At present, the evaluation methods of RECC mainly include entropy method and analytic hierarchy process (Zhao et al 2020, Jayanthi et al 2020, Diao et al 2019, strategic environmental assessment, and environmental impact assessment (Martinez-Grana et al 2014), ecological footprint method (Volodya et al 2018), fuzzy evaluation method (Yin et al 2020) (Cui et al 2019), logistic growth model (Streimikiene and Girdzijauskas 2008), artificial neural network model (Wu et al 2019, state-space model (Tang et al 2016), dynamic modeling (Lane et al 2014) and other models are used to evaluate the status quo and predict the future of RECC. The entropy-weight-based TOPSIS method has high credibility and accuracy and avoids the subjectivity of weight determination (Li et al 2019).…”

Temporal and spatial evolution and obstacle diagnosis of resource and environment carrying capacity in the Loess Plateau

“…The main methods for determining the weight of the RECC index system are entropy method and analytic hierarchy process [ 36 – 38 ]. The entropy-weight-based TOPSIS method has high credibility and accuracy, and avoids the subjectivity of weight determination [ 39 ]. Scholars have mainly used the fuzzy evaluation method [ 40 ], system dynamics model [ 41 ], logistic growth model [ 42 ], and state-space model [ 43 ] to build their assessment models.…”