Mohammad Ebrahim Banihabib

Comparison of Different Multi Criteria Decision-Making Models in Prioritizing Flood Management Alternatives

Recent increases in life loss, destruction and property damages caused by flood at global scale, have inevitably highlighted the pivotal considerations of sustainable development through flood risk management. Throughout the paper, a practical framework to prioritize the flood risk management alternatives for Gorganrood River in Iran was applied. Comparison between multi criteria decision making (MCDM) models with different computational mechanisms provided an opportunity to obtain the most conclusive model. Non-parametric stochastic tests, aggregation models and sensitivity analysis were employed to investigate the most suitable ranking model for the case study. The outcomes of these mentioned tools illustrated that ELimination and Et Choice Translating Reality (ELECTRE III), a non-compensatory model, stood superior to the others. Moreover, Eigen-vector’s performance for assigning weights to the criteria was proved by the application of Kendall Tau Correlation Coefficient Test. From the technical point of view, the highest priority among the criteria belonged to a social criteria named Expected Average Number of Casualties per year. Furthermore, an alternative with pre and post disaster effectiveness was determined as the top-rank measure. This alternative constituted flood insurance plus flood warning system. The present research illustrated that ELECTRE III could deal with the complexity of flood management criteria. Hence, this MCDM model would be an effective tool for dealing with complex prioritization issues.

A new framework for strategic planning to stabilize a shrinking lake

Abstract Preventing Lake Urmia, the sixth largest saltwater (endorheic) lake on the earth, from rapidly drying up is a paramount mission of the Iranian government. Thus, a new framework was proposed to develop a strategic plan to stabilize the shrinking lake, based on sustainable development criteria (SDC). We developed the strategies via the new hybrid Strategic Sustainable Planning Framework (SSPF), including a strength–weakness–opportunity–threat (SWOT) analysis matrix, SDC, multi-criteria decision making models (MCDMs), and sensitivity analysis. First, to develop aggressive, conservative, competitive, and defensive strategies, SSPF analyzed site-specific internal strengths and weaknesses plus external opportunities and threats. It then, preliminarily ranked the strategies by embedding SDC within MCDMs (Analytic Hierarchy Process [AHP]; Simple Additive Weighting [SAW]; and Technique for Order of Preference by Similarity to Ideal Solution [TOPSIS]). To promote confidence in selection of the strategy, sensitivity analyses of the results were also performed. Here, the selected strategy had the highest rank among the set of strategies, with the least sensitivity to the input data variations. SAW and AHP were the models least and most sensitive to SDC weight variations, respectively. According to sustainability analysis, the purely structurally oriented strategies were determined to be environmentally non-sustainable. Based on the results, we identified 2 conservative–competitive strategies that could stabilize the lake and prevent future losses. Adding MCDMs to the combination of SWOT and Quantitative Strategic Planning Matrix (SWOT-QSPM) enhanced the process of decision making. Hence, we recommend SSPF as a helpful approach in evaluating sustainable and strategic solutions to this and similar problems.

Fuzzy Hybrid MCDM Model for Ranking the Agricultural Water Demand Management Strategies in Arid Areas

In this paper, a fuzzy Multi Criteria Decision Making (MCDM) model is proposed for considering the uncertainty of expert’s opinion in decision making of agricultural water demand management. The performance of Analytical Hierarchy Process (AHP) model was evaluated, and the hybrid model of Modified Technique for Order of Preference by Similarity to Ideal Solution (MTOPSIS) and AHP are introduced in non-fuzzy, MTOPSIS-AHP (MTAHP), and Fuzzy MTOPSIS-AHP (FMTAHP) setting, and the result of them were compared. Sensitivity analyses of tested fuzzy MCDM model was carried out in 3 separate steps of aggregation of individual fuzzy judgments, fuzzy distances values for fuzzification and ranking of fuzzy numbers. In this research, Coefficient of Variation (CV) index of final rates was proposed to evaluate the performance of MCDM models. The results of this paper showed that the geometric mean method is better for aggregation of individual fuzzy judgments. The results showed that non-fuzzy proposed model, MTAHP provided better ranking resolution among tested non-fuzzy MCDM models and only the proposed FMTAHP model significantly increased the CV index of final rates and improve the results resolution. Finally, the proposed MTAHP and FMTAHP models introduced as the best models for ranking alternatives by high resolution in non-fuzzy and fuzzy environment, respectively.

Optimization of inter-sectorial water reallocation for arid-zone megacity-dominated area

Current water demand management of megacity-dominated areas in arid-zones should be revised to optimize water allocation for sustainable development. A novel hierarchical optimization model is proposed and examined for such an area in the arid zone of Iran. The model can significantly increase water economic efficiency, reduce unsatisfied demand, and maintain necessary agricultural production. Model sub-optimization provides the cropping pattern and transient four-phenological-stage deficit-irrigation strategy that maximizes economic benefit per unit agricultural allocation. The model employs a nonlinear benefit function for industry, a linear benefit function for service, and a cubic benefit function for deficit-irrigated agriculture. If available water is unchanged, optimal economic benefit increases 283 percent from the current situation. This requires decreasing agricultural water allocation, changing cropping patterns, using deficit irrigation, and increasing development of industrial and service sectors. If annual available water decreases by 25%, or by 40%, net economic benefit can increase 101% and 19%, respectively.

The impact of slit and detention dams on debris flow control using GSTARS 3.0

Debris flows, often the result of environmental degradation in mountainous areas, can be an extreme geological catastrophe. The concept of building detention dams to control debris flows has emerged after numerous deaths and huge economic losses have accumulated due to the destruction of infrastructure. Detention dams are well known for their efficient flow control and relatively low installation cost. However, their efficiency in decreasing peak flow is adversely affected by sedimentation, which not only decreases the effective lifetime of dams but also causes obstruction of outlets. In this research, the capability of GSTARS3.0 (Generalized Sediment Transport model for Alluvial River Simulation) was evaluated for a semi-three-dimensional simulation of sedimentation and flow routing in the reservoirs of two different kinds of detention dams: a classic detention dam and a slit dam. Sediment transport, scour, and deposition processes were simulated and calibrated along an experimental flume that represented the reservoirs of the detention dams to give a semi-three-dimensional variation of the bed geometry after debris flow events. Finally, models were applied to the Mojen River, Iran, as a real case. The model results convincingly show the capability of the GSTARS3.0 model to simulate sedimentation in reservoirs and the superiority of slit dams in controlling debris flow.

Performance of conceptual and black-box models in flood warning systems

Abstract Flood forecasting is a core of flood forecasting and flood warning system which can be implemented by both conceptual rainfall–runoff (CRR) model and black-box rainfall–runoff (BBRR) model. Dynamic artificial neural network (DANN) as an innovative BBRR model and HEC-HMS as a traditional CRR model were used for flood forecasting. The aim of this paper is to compare the efficiency of HEC-HMS and DANN for the determination of flood warning lead-time (FWLT) in a steep urbanized watershed. A framework is proposed to compare the performance of the models based on four criteria: type and quantity of required input data by each model, flood simulation performance, FWLT and expected lead-time (ELT). Finally, the results show that FWLT and ELT were estimated longer by DANN than by HEC-HMS model. In brief, because of less required data by BBRR model and its longer ELT, future research should be focused on better verification of it.

Simulation of Ca2+ and Mg2+ removal process in fixed-bed column of natural zeolite

AbstractIn this research, an explicit finite difference scheme is assessed for solving non-linear governing differential equation of contaminant transport to simulate the removal process of Ca2+ and Mg2+ within a fixed-bed zeolite column. Experiments were carried out in a continuous system at pH 7.5, with five samples at concentrations of 120, 50 ppm for Ca2+ and Mg2+, respectively. The required parameters for Langmuir isotherms were obtained using batch experiments. The distribution coefficient of linear adsorption isotherm was evaluated through calibrating the proposed numerical model. In the calibration, the parameter adjusted to obtain the most suitable distribution coefficient by which outflow solution concentration best fits the experimental results. Furthermore, the dispersion coefficient was evaluated using an empirical relationship to calculate dispersivity. The comparison of the proposed numerical model and experimental results indicated that the proposed numerical model that uses linear adsorpt...

Locating Water Desalination Facilities for Municipal Drinking Water Based on Qualitative and Quantitative Characteristics of Groundwater in Iran's Desert Regions

Groundwater resources have become the main resources for water supply due to the unavailability of surface water in arid zones. Arid zone’s damage to groundwater resources will have a high impact on human life in arid zones comparing to other regions. Due to the lack of surface water resources in these arid zones, groundwater is used as a resource for drinking and sanitation purposes due to the lack of surface water resources in these arid zones. Water desalination facilities are set up in locations where there is both sufficient amount of water (quantitative criteria) and the extracted water has adequate quality (qualitative criteria). Therefore, an optimization model should be used to locate optimal places for water desalination facilities. Multi-criteria decision-making models are mathematical techniques that, by using the geographic information system, are able to evaluate the options under complicated and indefinite geographic conditions. This research prepares information and factor maps to assign weights to qualitative water maps which were combined in the form of an inductive network. Therefore, by employing the concept of fuzzy fusion models, this article presents a method for solving multi-criteria geographically-indeterminate problems, and finally finds an appropriate location for the construction of a water desalination system in the desert region of Birjand in Iran.

Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow

Accurate reservoir-inflow forecasting is especially important for optimizing operation of multi-propose reservoirs that provide hydropower generation, flood control, and water for domestic use and irrigation. There are no previous reports of successful daily flow prediction using a 1-year lead-time. This paper reports successful daily stream flow predictions for that extended lead-time. It presents the first NARX (Nonlinear Auto Regressive model with eXogenous inputs)-type recurrent neural network (NARX-RNN) model used to forecast daily reservoir inflow for a long lead-time. It is the first use of dynamic memory to extend the forecast lead-time beyond the previously reported 1-week lead-times. For new nonlinear NARX-RNN models, we present and test 1600 alternative structures, differing in transfer functions (2), and numbers of inputs (2 to 5), neurons per hidden layer (1 to 20), input delays and output delays. For predicting inflow to the reservoir of the multi-purpose Dez Dam, we contrast accuracies of forecasts from the new models, and from a conventional auto-regressive linear ARIMA model. Based upon normalized root-mean-square error RMSE/Q¯obs