SIPRI Policy Report: Post-conflict Reconstruction in the Nineveh Plains of Iraq

Post-conflict Reconstruction in the Nineveh Plains of Iraq: Agriculture, Cultural Practices and Social Cohesion

Authors: Amal Bourhrous, Shivan Fazil and Dylan O’Driscoll (SIPRI)

November, 2022

Stockholm International Peace Research Institute


The atrocities committed by the Islamic State (IS) between 2014 and 2017 left deep scars on the Nineveh Plains in northern Iraq. IS deliberately targeted ethnic and religious communities with the aim of erasing the traces of diversity, pluralism and coexistence that have long characterized the region. To prevent people from living as Assyrians, Chaldeans, Kaka’i, Shabaks, Syriacs, Turkmen and Yazidis, IS destroyed sites of cultural and religious significance to these communities and devastated their livelihoods, including their crop and livestock farming activities.

Using a people-centered approach, this SIPRI Research Policy Report stresses the need for a holistic approach to post-conflict reconstruction in the Nineveh Plains that not only focuses on rebuilding the physical environment and economic structures but also pays adequate attention to restoring the ability of communities to engage in cultural and religious practices, and to mending social and intercommunity relations. The report highlights the interconnectedness of physical environments, economic structures, cultural practices and social dynamics. It stresses the need to address the impacts of the IS occupation while taking into account other pressing challenges such as climate change and water scarcity.

New article on Predicting Groundwater Footprints in Iran With Machine Learning

Using machine learning to determine acceptable levels of groundwater consumption in Iran

Authors: Ronny Berndtsson (Lund University), Sami Ghordoyee Milan (University of Tehran), Zahra Kayhomayoon (Payame Noor University), Naser Arya Azar (University of Tabriz), Mohammad Reza Ramezani (Griffith University), and Hamid Kardan Moghaddam (Water Research Institute Iran).

November, 2022

Journal Sustainable Production and Consumption


Groundwater footprint index (GFI) is an essential indicator to assess the sustainability of groundwater aquifers. Prediction of future GFI can significantly help managers and decision-makers of groundwater supply to better plan for future resilient consumption of surface and groundwater. In this context, artificial intelligence and machine learning models can aid to predict GFI in view of lacking or uncertain data. We used this technique to predict GFI for 178 Iranian aquifers. To our knowledge, this is the first time that GFI was predicted using machine learning models. Four models, i.e., adaptive neuro-fuzzy inference system, least-squares support vector regression, random forest, and gene expression programming, were used to predict GFI. Systematic combinations of eight variables, including precipitation, recharge, return water, infiltration from the river to the aquifer, groundwater exploitation, aquifer area, evaporation, and river drainage from the aquifer were used in the form of nine input scenarios for GFI prediction. The results showed that inclusion of all input variables gave the best results for predicting the GFI. Predicted GFIs were generally between 0.5 and 8 with an average of 1.9. A value above 1 indicates that groundwater consumption is not resilient that can adversely affect available groundwater resources in the future. Over-use of groundwater can lead to land subsidence. Especially, aquifers located in Qom, Qazvin, Varamin, and Hamedan provinces of Iran may be affected due to large over-use. Among the four models, least-squares support vector regression resulted in the highest prediction performance. Due to the poor performance of adaptive neuro-fuzzy inference system, the novel Harris hawks optimization algorithm was used to improve the performance of adaptive neuro-fuzzy inference system. The Harris hawks optimization – adaptive neuro-fuzzy inference system hybrid model improved the GFI prediction performance. Machine learning methods improve prediction of GFI for aquifers and thus, can be used to better manage groundwater in areas with less reliable data.

New article on landscape composition modeling in Iran

Analysis of Landscape Composition and Configuration Based on LULC Change Modeling

Authors: Hossein Hashemi (Lund University) Masoomeh Yaghoobi (Shahid Beheshti University), Alireza Vafaeinejad (Shahid Beheshti University), and Hamidreza Moradi (Tarbiat Modares University).

November, 2022

Journal Sustainability


Land cover changes threaten biodiversity by impacting the natural habitats and require careful and continuous assessment. The standard approach for assessing these changes is land cover modeling. The present study investigated the spatio-temporal changes in Land Use Land Cover (LULC) in the Gorgan River Basin (GRB) during the 1990–2020 period and predicted the changes by 2040. First, a change analysis employing satellite imagery from 1990 to 2020 was carried out. Then, the Multi-Layer Perceptron (MLP) technique was used to predict the transition potential. The accuracy rate, training RMS, and testing RMS of the artificial neural network, MLP, and the transition potential modeling were computed in order to evaluate the results. Utilizing projections for 2020, the prediction of land cover change was made. By contrasting the anticipated land cover map of 2020 with the actual land cover map of 2020, the accuracy of the model was evaluated. The LULC conditions in the future were predicted under two scenarios of the current change trend (scenario 1) and the ecological capability of the land (scenario 2) by 2040. Seven landscape metrics were considered, including Number of Patches, Patch Density, the Largest Patch Index, Edge Density, Landscape Shape Index, Patch Area, and Area-Weighted Mean Shape Index. Based on the Cramer coefficient, the most critical factors affecting LULC change were elevation, distance from forest, and experimental probability of change. For the 1990–2020 period, the LULC change was shown to be influenced by deforestation, reduced rangeland, and expansion of agricultural and residential areas. Based on scenario 1, the area of forest, agriculture, and rangeland would face −0.8, 0.5, and 0.1% changes in the total area, respectively. In scenario 2, the area of forest, agriculture, and rangeland would change by 0.1, −1.3, and 1.3% of the total area, respectively. Landscape metrics results indicated the destructive trend of the landscape during the 1990–2020 period. For improving the natural condition of the GRB, it is suggested to prioritize different areas in need of regeneration due to inappropriate LULC changes and take preventive and protective measures where changes in LULC were predicted in the future, taking into account land management conditions (scenario 2).

New article on landfill site selection for solid waste in Iran

Optimal Landfill Site Selection for Solid Waste of Three Municipalities Based on Boolean and Fuzzy Methods: A Case Study in Kermanshah Province, Iran

Authors: Seyed Amir Naghibi (Lund University), Seyed Mohsen Mousavi (Shahid Beheshti University), Golnaz Darvishi (Shahid Beheshti University), and Naghmeh Mobarghaee Dinan (Shahid Beheshti University).

October, 2022

Journal land


In recent decades, population increase and urban development have led to catastrophic environmental consequences. One of the principal objectives to achieve “sustainable development” is to find suitable landfills. Due to their physical characteristics, which have led to a lack of landfill sites and closeness to water bodies, agricultural fields, and residential areas, the cities of Javanrood, Paveh, and Ravansar were chosen as the necessary research regions. On the other hand, these landfills are unable to accommodate the growing urban population. Therefore, this study attempts to develop a framework for spotting the most suitable sites for landfill construction with these three cities as case studies. For this, 10 important driving factors (9 factors and 1 constraint) in landfill site selection were generated. Second, for the fuzzy membership function, the analytic hierarchy process (AHP) method was employed for the standardization of criteria and determining the weight of the driving factors. Then, the Boolean, weighted linear combination (WLC) and ordered weighted average (OWA) methods were utilized to spot optimal sites for landfills. Finally, two suitable sites were found for landfills: site (a) was obtained from the WLC, and site (b) was obtained from OWA-low risk some trade-off (LRST) methods. Our results proved the high efficiency of multi-criteria decision-making methodology for landfill site selection.