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Several models with a variety of concepts and approaches have been proposed to address different aspects of the Water-Energy-Food (WEF) nexus system. In some models, the interaction between WEF resources is considered without considering the internal relationships between subsystems and vice versa. This is while, a comprehensive model should consider all internal and external relationships between the three subsystems which are named interrelations and interactions, respectively. Therefore, this study was an initial step to introduce the holistic WEF nexus simulator framework and its components. In a holistic model, it is axiomatic that extensive data should be gathered. Hence, in the second step, the authors attempted to classify the huge amount of required data into two distinct categories: (1) non-simulated data (independent parameters, independent variables, and management parameters) and (2) simulated data (IFs and THENs). In addition to providing valuable feedback on the WEF nexus concept and providing required data for policy evaluation and assessment, listing and classifying the required data and describing them in terms of IFs and THENs will provide valuable insight. This study shows how extensive data can be accessed and shared within a comprehensive nexus simulation model. As a result of using this classification method, the interrelations between each subsystem and the interactions with other subsystems in the nexus model can be extracted simply, and none of them will be overlooked due to lack of knowledge about the nexus system. Additionally, these IFs and THENs variables are considered as a great solution to diminish the complexity of the nexus system to implement it.

The interactions and trade-offs between Water, Food, and Energy (WFE) have recently attracted the attention of researchers worldwide. A new approach called nexus has been used to examine these interactions in an integrated way. A significant obstacle to adopting the WFE nexus is the lack of a comprehensive and easy-to-use simulation model. By reviewing the articles in Scopus and Google Scholar databases, WFE nexus studies can be divided into two categories: simulation-based and conceptual-based studies of WFE nexus. Based on developmental perspective on food subsystem modeling in WFE nexus, the conceptual studies excluded and the modeling studies reviewed. Two points of view can be used for WFE nexus modeling: 1. Hard-link modeling and 2. Soft-link modeling. Comparing these two types of modeling showed that Hard link modeling cannot model the interrelations of the food subsystem and this shortcoming is of great importance. Agriculture is the primary source of food supply because livestock and poultry products are also indirectly dependent on agricultural products. This study reviewed the crop growth models (CGMs) used in the WFE nexus system from the development perspective. The technical characteristics of the CGMs have been evaluated according to the requirements of the CGMs. Finally, a checklist based on the criteria defined for the nexus system has been provided, which can guide researchers in choosing the appropriate simulation model for the food subsystem with the nexus approach. Also, future research suggestions were recommended to develop a food subsystem simulation model based on nexus system approach criteria.

Water, Energy, and Food (WEF) are inseparably interrelated vital resources for every society's subsistence, sustainability, and development. Due to world population growth and economic developments, new environmental challenges that aggravate WEF resources' insufficiency have emerged. WEF nexus aims to examine how three WEF subsystems will interact and grow together. Most existing energy models do not fully account for all requirements and interactions between the WEF subsystems in the WEF nexus context. This paper frames a comprehensive structure for the energy subsystem within the WEF nexus system for Iran country. In the proposed framework, the energy subsystem's supply and demand side are formulated using both data and equations. The presented framework addresses most interactions between WEF subsystems in a dynamic and adaptive setting. It is shown that through analysis of binding interactions between WEF, different management scenarios can boost the flexibility of the supply and demand side of the energy subsystem. In addition, by incorporating this framework, the water subsystem will manage the allocated and consumed water on the supply side and arrive at the most desirable outcome for the water sector. Also, the optimal cropping pattern could be evaluated based on energy consumption.

The current study introduces a conceptual socio-hydrological-based framework for the water-energy-food (WEF) nexus. The proposed conceptual framework aims to investigate how farmers' dynamic agricultural activities under different socio-economic conditions affect the WEF systems. The WEF nexus model has been integrated with an Agent-Based Model, reflecting the farmers’ agricultural activities. Furthermore, the agent-based model benefits from Association Rule Mining to define farmer agents’ agricultural decision-making in various conditions. The processes within the WEF nexus are simultaneously physical, socio-economic, ecological, and political. Indeed, there are interrelated interactions among the mentioned processes in ways that have not yet been properly delineated and mapped. Thus, to obtain sustainable outcomes, the current study investigates trade-offs among natural resources and social systems in the WEF nexus approach. The proposed socio-hydrological WEF nexus framework may provide more in-depth future insights for policy-makers through capturing bidirectional feedbacks among farmers and WEF systems. In other words, the proposed framework can help policymakers to capture the dynamic impacts of agricultural activities by farmers on the WEF nexus, which may vary due to different socio-economic conditions.

Groundwater overdraft in many regions throughout the world has been threatening the sustainability of this valuable resource. It has been argued that climate change may contribute to the severity of the issue; hence “impact assessment” is being replaced by “adaptation,” which explores more adapting scenarios and approaches. This study explores the adaptability of the proposed cyclic and non-cyclic conjunctive use of groundwater and surface water resources in increasing groundwater sustainability while increasing the sustainability of water allocation to the agricultural sector under possible climate change scenarios. To simulate climate change in the study area, precipitation and temperature variables are extracted from the results of three global atmospheric circulation models (Ensemble, CMCC-CMS, MRI-CGCM3) under RCP2.6 and RCP8.5 greenhouse gas emission scenarios in the period of 2021–2031. Spatial downscaling is performed using the M5 decision tree algorithm. The Wavelet-M5 hybrid model is used to predict runoff values as a rainfall-runoff model. Also, the Kharrufa method is applied to calculate evaporation in the future seasons. The system's adaptability to climate change is examined using the multi-objective cyclic and non-cyclic conjunctive use of surface and groundwater models. The study reveals that cyclic operation strategy improves the conjunctive use system adaptability compared to the optimal operation strategy that employs the non-cyclic approach. In this study's case study, the improvement in groundwater sustainability index exceeds 27 percent over the non-cyclic conjunctive use strategy.

Recently, research literature and policy settings have increasingly emphasized the necessity of water-energy-food (WEF) interaction to evaluate a nexus system thoroughly and, as a result, make comprehensive decisions. This study thoroughly evaluated energy subsystem simulations, focusing on the role of energy within the WEF nexus system. The energy model using the WEF nexus approach, on the other hand, has not yet been reviewed. Multidimensional insights will be provided for the proper usage of energy models in the context of the WEF nexus model through examinations of energy subsystem simulation models inside the WEF nexus approach from previous research. This study provides a systematic review of 459 articles regarding energy simulation modelling issues relating to the WEF nexus system. The energy subsystem simulation in the WEF nexus system is modelled in both online (one-way) and offline modes (Two-way). The review highlighted that the energy simulation in the WEF nexus system simulation struggles to represent its system boundary compatible with the water and food subsystems. Additionally, the return flow from cooling towers and energy consumption of different crops did not address comprehensively to make a holistic decision within the WEF nexus system.

Rainfall-runoff (r-r) modeling at different time scales is considered as a significant issue in hydro-environmental planning. As a first hydrological implementation, for one-time-ahead r-r modeling of two watersheds with totally distinct climatic conditions, Genetic Algorithm (GA, as a global search technique) and Emotional Artificial Neural Network (EANN, as a new production of Artificial Intelligence (AI) based methods that simulated based on the brain neurophysiological structure) was combined. Determining the optimal architecture of AI-based networks is vital for increasing the accuracy of prediction by the network and also to reduce run-time. In the current study, GA has been implemented to choose the important features candidate as EANN input and automatically diagnose the optimal number of hidden nodes and hormones simultaneously. The acquired results indicated a better representation of the proposed hybrid GA-EANN model compared to the sole ANN and EANN. Numerical identification of obtained results revealed that the proposed hybrid GA-EANN model might enhance the better results than the EANN model up to 19% and 35% in terms of testing suitability criteria for Aji Chai and Murrumbidgee catchments, respectively.

The current study presents an efficient method for deriving precise operation rules from all subsystems of a distributed conjunctive use system (CUS), including aquifer, river, and reservoir. Distributed aquifer simulation has been performed using the URM method. Given that the historical flow time series can only represent one of the possible situations in the future and its use to determine the performance of the CUS is certainly not very reliable, in this study, river flow uncertainties are implicitly considered. To develop the operation rules, the time series of river flow were generated using autoregressive model. Then, the operation optimization model of the system was implemented with the objective function of minimizing water shortage for different river flow time series. 70% of the data was used for model training and 30% for model validation. Finally, using the decision tree algorithm (M5Rules), the conditional operation rules were extracted and compared with the single linear regression operation rules. Using five efficiency criteria CC, MAE, RMSE, RAE, and RRSE, the comparison of conditional and single linear regression operating rules has been done. The results showed that the the conditional operation rules reduces relative absolute error by a minimum of 39% and a maximum of 71%. If the system is operated according to the conditional rules, in the worst case, the amount of water shortage imposed will be 16.61 MCM over ten years.

The study of non-stationary effects of hydrological time series and land-use changes in urban areas is essential to predict future floods and their probable damage. In the current study, a novel method was proposed for analyzing their simultaneous impact. For this purpose, rainfall frequency and land-use changes analyses were conducted for two different long-term periods, and the results were compared. Then, hydrologic modeling of the catchment was carried out using the HEC-HMS model, and obtained hydrographs were fed to the HEC-RAS2D model for estimating flood inundation areas. Using the economic information of assets and their damage functions, flood damages related to these two periods were evaluated through the HEC-FIA model. The results indicated that in the low return periods (e.g., 2-year flood), the damage in the second period was increased with respect to the first one but increased for the return periods of 5 to 100 years. Furthermore, surface runoff showed a 4.65% increase due to land-use change and a 12% increase due to rainfall non-stationarity. Moreover, flood damage showed a 136% increase on average, and among the two studied factors, the non-stationarity of rainfalls is considerably more effective on flood intensification.

The primary impediment to adopting the Water, Energy, and Food (WEF) Nexus is a lack of a comprehensive and user-friendly simulation model. According to our search on Google Scholar and the Scopus databank, WEF Nexus studies can be divided into three broad categories: (1) studies about the nexus concept, (2) studies related to nexus modeling and software development, and (3) case studies. Given that the present study's objective is to review various solutions for WEF Nexus modeling and also to prepare a checklist of available models to find a better model for nexus simulation, we excluded papers and studies which were related to the nexus concept. After that, we split up other papers that talked about nexus and software development into (1) integrated and (2) compiled approaches. Then, it was attempted to identify the shortcomings in each approach. It was shown that the existing integrated WEF Nexus models (such as MUSIASEM, NexSym, CLEW, and ANEMI) had some significant drawbacks compared to compiled alternatives. Several of the major shortcomings of existing integrated models include the following: (1) They did not cover all spatial scales; (2) they included only a limited number of interactions across WEF subsystems; and (3) some of these models were unavailable. Therefore, as a general result of the current study, it was shown that compiled approach is generally preferable compared to available integrated models. In this regard, we tried to find the best water simulation models to implement in the nexus concept. We searched for papers about water simulation models and defined water subsystem requirements in the nexus concept. So, we evaluated each water simulation model based on its ability to cover water subsystem requirements. This work illustrates the capability of a suitable water simulation model to be utilized in the nexus concept and provides a holistic checklist to choose the preferred water simulation model based on the needs of each issue.

This study proposes a novel socio-hydrological modeling framework for assessing the performance of complex water resources systems. It employs and integrates agent-based modeling (ABM) and the theory of planned behavior (TPB) into the socio-hydrological modeling framework to account for agents’ behaviors. Due to farmers’ major role in anthropogenic droughts, this paper mainly focuses on farmers’ behavior. The TPB framework and the agents’ behavioral rules in ABM are structured based on the data obtained from field questionnaires and interviews by the farmers in the Zarrineh River Basin as the main river feeding the Urmia Lake. The proposed modeling framework, including the TPB and ABM structures and rules, are validated by comparing the results of the proposed socio-hydrological simulation model with the actual data on the simulation period. To assess the ability of the proposed framework, root mean square error (RMSE) and the determination coefficient (DC) were computed. The results show the acceptable performance of the proposed model by RMSE = 0.18 and DC = 0.92. Analysis of the results indicates that financial conditions, farmland size, farmers’ age, and farmers’ education are among the major factors in farmers’ decision-making process, respectively.

The target of the current paper was to examine the performance of three Markovian and seasonal based artificial neural network (ANN) models for one-step ahead and three-step ahead prediction of monthly precipitation which is the most important parameter of any hydrological study. The models proposed here are feed forward neural network (FFNN, as a classic ANN-based models), Wavelet-ANN (WANN, as a hybrid model), and Emotional-ANN (EANN, as a modern generation of ANN-based models). The models were used to precipitation prediction of seven stations located in the Northern Cyprus. Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps, while in scenario 2, the central station’s data were also imposed into the models in addition to each station’s data, as exogenous inputs. The obtained results showed the better performance of the EANN model in comparison with other models (FFNN and WANN) especially in three-step ahead prediction. The superiorities of the EANN model over other models are due to its ability in dealing with error magnification in multi-step ahead prediction. Also, the results indicated that the performance of the scenario 2 was better than scenario 1, showing improvement of modeling efficiency up to 17% and 26% in calibration and verification steps, respectively.

The suspended sediment load (SSL) modeling generated within a catchment is a significant issue in the environmental and water resources planning and management of watersheds. The estimation methods of SSL are limited by the important parameters and boundary conditions (which are based on the flow and sediment properties). In this situation, soft computing approaches have proven to be an efficient tool in modelling the sediment load of rivers. In this study, the hybrid Wavelet-M5 model was introduced to model SSL of two different rivers (Lighvanchai and Upper Rio Grande) at both daily and monthly scales. In this way, first, the runoff and suspended sediment load time series were decomposed using the wavelet transform to several sub-time series to handle the non-stationary of the runoff and sediment time series. Then, the obtained sub-series were applied to M5 model tree as inputs. The obtained results for the Upper Rio Grande River at daily time scale, showed the better performance of Wavelet-M5 model in comparison with individual Artificial Neural Network (ANN) and M5 models so that the obtained Nash-Sutcliffe efficiency (NSE) was 0.94 by the hybrid Wavelet-M5 model while it was calculated as 0.89 and 0.77 by Wavelet-ANN (WANN) and M5 tree models, respectively. Also, the obtained NSE for the Lighvanchai River at monthly time scale was 0.90 by the hybrid Wavelet-M5 model while it was calculated as 0.78 and 0.69 by Wavelet-ANN (WANN) and M5 tree models in the verification step, respectively.

The current study introduces a conceptual socio-hydrological based framework for water-energy-food (WEF) nexus. The proposed conceptual framework aims to investigate how farmers' dynamic agricultural activities under different socio-economic conditions affect the WEF systems. The WEF nexus model has been integrated with an Agent-Based Model, reflecting the farmers’ agricultural activities. Furthermore, the agent-based model benefits from Association Rule Mining to define farmer agents’ agricultural decision-making in various conditions. The processes within the WEF nexus are simultaneously physical, socio-economic, ecological, and political. Indeed, there are interrelated interactions among the mentioned processes in ways that have not yet properly delineated, mapped, or even perceived. Thus, for obtaining sustainable outcomes, the current study attempts to investigate trade-offs among natural resources and social systems in the WEF nexus approach. The proposed framework may provide more in-depth future insights for policy-makers through capturing bidirectional feedbacks among farmers and WEF systems. Furthermore, the proposed socio-hydrological WEF nexus framework can be adapted and applied to various societies and environments to provide more in-depth future insights for policy-makers through capturing bidirectional feedbacks among farmers and WEF systems.

In recent decades, global forecasts show that the demands for freshwater, energy, and food have been affected by population growth, economic development, international trade, and increasing urbanization and food diversity. Moreover, cultural changes and other phenomena have adversely affected the water, energy, and food (WEF) resources demand. Consequently, climate change adverse impact, which is an undesirable phenomenon, will be increased at a staggering rate. Numerous studies have emphasized that the lack of integrated and systematic management strategies threaten these indispensable resources' ability to meet growing demand. In many countries, the security of WEF resources, which is related to each other, has become three non-traditional security challenges that overshadow these communities' development. Before realizing the importance of interactions between these three vital resources, the management strategies were typically used for one source, which was completely independent of the other two. Since these strategies did not consider the interactions between the three systems, the applied strategies' results were sometimes contradictory. Also, due to the lack of attention to these three sectors' vital connections, the competition level among these systems has been increased. Therefore, in recent years, the study of the complex interactions and connections between these resources has led to the emergence of a new term called WEF nexus in the scientific communities. Most studies about the WEF nexus have only described the current situation or, ideally, analyzed several predetermined scenarios. However, the adopted approach should be able to analyze different scenarios and be efficient and robust enough to develop a variety of strategies and utilize them for macro-policies. In this study, after reviewing the complex interactions between the WEF systems, it was tried to introduce a novel paradigm for the WEF nexus, which concentrates on expanding the possible space of WEF nexus.

In recent decades, global forecasts show that the demands for freshwater, energy, and food have been affected by population growth, economic development, international trade, and increasing urbanization and food diversity. Moreover, cultural changes and other phenomena have adversely affected the water, energy, and food (WEF) resources demand. Consequently, climate change adverse impact, which is an undesirable phenomenon, will be increased at a staggering rate. Numerous studies have emphasized that the lack of integrated and systematic management strategies threaten these indispensable resources' ability to meet growing demand. In many countries, the security of WEF resources, which is related to each other, has become three non-traditional security challenges that overshadow these communities' development. Before realizing the importance of interactions between these three vital resources, the management strategies were typically used for one source, which was completely independent of the other two. Since these strategies did not consider the interactions between the three systems, the applied strategies' results were sometimes contradictory. Also, due to the lack of attention to these three sectors' vital connections, the competition level among these systems has been increased. Therefore, in recent years, the study of the complex interactions and connections between these resources has led to the emergence of a new term called WEF nexus in the scientific communities. Most studies about the WEF nexus have only described the current situation or, ideally, analyzed several predetermined scenarios. However, the adopted approach should be able to analyze different scenarios and be efficient and robust enough to develop a variety of strategies and utilize them for macro-policies. In this study, after reviewing the complex interactions between the WEF systems, it was tried to introduce a novel paradigm for the WEF nexus, which concentrates on expanding the possible space of WEF nexus.

In this paper, artificial neural network (ANN) was used for statistically downscale the outputs of general circulation models (GCMs) to assess future changes of precipitation and mean temperature in Tabriz synoptic station at north-west of Iran. Since one of the significant subjects in statistical downscaling of GCMs is to select the most dominant large scale climate variables (predictors) among huge number of potential predictors, the predictors screening methods including decision tree, mutual information (MI) and correlation coefficient (CC) were used to statistically downscale mean monthly precipitation and temperature. Three GCMs were used, including Can-ESM2 and BNU-ESM from IPCC AR5 models and CGCM3 from IPCC AR4 models. The results of downscaling in the base period (1951–2000) indicated that among feature extraction methods decision tree had superiority to MI and CC techniques. Therefore, the future projection of precipitation and mean temperature during 2020–2060 was implemented using ANN-based simulation according to the most efficient downscaling model (i.e., decision tree-based calibration). Different results according to different GCMs and scenarios were obtained for precipitation projection. In this way, the Can-ESM2 model under RCP8.5 showed 29.78% decrease in annual precipitation and CGCM3 model under B1 indicated 1.06% increase of annual precipitation. Temperature projection outcomes denoted that annual mean temperature will increase over the region and the most increase in mean temperature was determined by BNU-ESM model under RCP8.5.

This study introduced a new hybrid model (Wavelet-M5 model) which combines the wavelet transforms and M5 model tree for rainfall-runoff modeling. For this purpose, the main time series were decomposed to several sub-signals by the wavelet transform, at first. Then, the obtained sub-time series were imposed as input data to M5 model tree, and finally, the related linear regressions were presented by M5 model tree. This new technique was applied on the monthly time series of Sardrud catchment and the results were also compared with other models like WANN and sole M5 model tree. The results showed that the accuracy of the proposed model is better than the previous models and also indicated the effect of data pre-processing on the performance of M5 model tree. The determination coefficient of the training stage was 0.80 and improved 31% than the M5 model tree for Sardrud catchment which is recognized as a normal watershed with a regular four seasons' pattern.

The hydrological time series have three principle components (autoregressive, seasonality and trend) and the performance of the models is strongly related to the nature of these components. The current research examines the accuracy of two Artificial Neural Network (ANN) based approaches for rainfall-runoff (r-r) modeling of two catchments with different geomorphological conditions at monthly and daily time scales. The techniques proposed here are hybrid wavelet-ANN (WANN) model, as a multi-resolution forecasting tool and Emotional Artificial Neural Network (EANN) (a new generation of ANN based models) which serves artificial emotional factors as well as classic bias and weights parameters. The obtained results for monthly modeling show that WANN could perform better than the simple feed forward neural network (FFNN) model up to 40% and 35% in terms of verification and training efficiency criteria due to significant seasonality involved in the monthly time series of the process. On the other hand, the obtained results for daily modeling via FFNN and EANN, both as Markovian models, indicates the superiority of EANN over FFNN because of EANN capability to better learning of extraordinary and extreme conditions of the process in the training phase.

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.

In this paper, the application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between several thresholds of monthly precipitation (MP) values of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend sea surface temperature (SST) of the Black, Mediterranean and Red Seas. Two major steps of the modeling in this study were the classification of de-trend SST data and selecting the most effective groups and extracting hidden predictive information involved in the data. The decision tree techniques which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and lift measures were calculated and compared for different thresholds of precipitation at different lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast extreme precipitation events considering higher threshold values and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and maximum MP of Tabriz and Kermanshah synoptic stations so that the confidence between the threshold of 35% of MP values and the de-trend SST of seas is higher than 70 for Tabriz and 60% for Kermanshah. It was also shown that the geographical location of stations and the distribution of precipitation data affect the measures of the rules and forecasting outcomes.

In this study, the hybrid wavelet-M5 model was introduced to model the rainfall-runoff process via three different data division strategies (75%–25%, 60%–40%, and 50%–50%) for two different...

Emotional artificial neural network (EANN) is a cutting-edge artificial intelligence method that has been used by researchers in the engineering and medical sciences over the recent years. First introduced in the 1999s, EANN is the combination of physiological and neural sciences for investigation of complex processes. Rainfall-runoff is a complex hydrological process that may be modeled by EANN methods to attain information about the response of a catchment to a rainfall event. In practice, the response is surface runoff either in the form of streamflow or flood in the catchment of interest. Thus, a reliable rainfall-runoff model is an inevitable component of a watershed so that decision-makers may use it to reduce the relevant vulnerability against extreme rainfall events. Undoubtedly, one way to empower the capabilities of rainfall-runoff models is the integration of recent achievements in the Internet of Things (IoT) with robust modeling algorithms such as EANN. Relying on the huge amount of knowledge within IoT components, the hybrid IoT-EANN can yield in the high-resolution space-time estimations of runoff that is a practical way to mitigate potential hazards of flooding through real time or in advance actions. With this chapter, we provide a short overview of the state-of-the-art EANN and its application in rainfall-runoff modeling. In addition, a concise review of the applications of IoT in hydro-environmental issues is provided. The chapter reveals that integrations of IoT with hydro-environmental studies are in their infancy. Being a new class of investigation, there is no hybrid rainfall-runoff model within the literature coupling IoT technology with artificial intelligence. No sponsor

In order to optimize the operation rule curve of the Shahrchay reservoir in the north-west of Iran under climate change, a new integrated simulation-optimization framework under different climate change scenarios was introduced in the current study. The proposed framework contains several steps: First, the more accurate general circulation models (GCMs) were chosen among several available GCMs and the predictors screening based on decision tree model (M5) was applied to select the most important predictors among enormous number of potential large-scale climate variables of GCMs. Then, the artificial neural networks (ANNs) were trained by the observed temperature and precipitation time series and the selected dominant predictors to downscale the precipitation and temperature parameters of GCMs for the base period (1951–2000) over the case area and the future monthly predictions of temperature and precipitation were implemented by the trained ANNs under A1B, B1, RCP4.5, and RCP8.5 climate change scenarios to assess the future changes of precipitation and temperature between 2020 and 2060. Thereafter, the estimated precipitation values were imposed to the hybrid Wavelet-M5 model to simulate inflow (runoff) to the Shahrchay reservoir and finally, genetic algorithm (GA) was used in order to optimize the operation rule curves of the reservoir system using the predicted average monthly precipitation, evaporation, inflow and considering water supply for agricultural and municipal targets and minimization of total squared deficiencies for future. The Wavelet-M5 model could result in a reliable performance in the one-step-ahead runoff prediction as the obtained correlation coefficient (CC) for training and verifying data sets were 98% and 97.7%, respectively. The results showed that the average long-term annual runoff volume may be decreased between 0.08% and 2.27% in the future with regard to the base period, and the simulation results for present and future conditions indicated a decrease in water availability. Also, the estimated optimal rule curves for the reservoir showed a different shape for all scenarios employed in this study.

In this paper, artificial neural network (ANN) was used for downscaling the outputs of general circulation models (GCMs) to evaluate changes in precipitation and mean temperature for a future period in Urmia at the north-west of Iran. MIROC-ESM-CHEM from IPCC AR5 was selected as an acceptable model based on correlation coefficient (CC) values, which is calculated between precipitation of GCM models and precipitation data prepared by Urmia Meteorological Organization for 1951-2000. As a first step, the most important parameters of the MIROC-ESM-CHEM were selected before the downscaling process by ANN in the base period (1951-2000). Afterward, the future projections of precipitation and mean temperature during 2020-2060 were applied using ANN-based simulation according to the CC method. By comparing the results, the MIROC-ESM-CHEM showed a 2.01% increase under RCP4.5 and a 0.16% decrease under RCP8.5 in annual precipitation. Also, the temperature projection outputs showed the annual mean temperature would increase in the future period in this area, and it is likely to get warmer.