Globally, landslide events are progressively increasing resulting in adverse impacts on people’s lives, economy, and the environment at large (Collins & Jibson, 2015; Kirschbaum et al., 2015). Landslides are events where the mass of rocks, soil, or debris falls down a slope (Li & Chen, 2020; Silalahi et al., 2019). They include sliding and falling of earth’s materials due to effects of gravity (Das, 2011; Motamedi, 2013) triggered by human activities that alter the surface slope which leads to instability of the area hence slope failure (Agliardi et al., 2012). Effects due to landslides events globally are critical (Chen & Chen, 2021; Díaz et al., 2020). However, the impacts are more likely to increase due to growth in population and increase in human activities in recent times as people continue to expand their settlements and other activities thus, infringing and encircling vulnerable high ground that are prone to failure (Tegeje, 2017). Landslides does not only affects human production and life but sometimes has a great influence on human society (Chen & Chen, 2021; Froude & Petley, 2018; Haque et al., 2019) and the environments (Chalise et al., 2019; Turner, 2018). It is estimated that worldwide from 1998 to 2017, landslides accounted for 5.2% of the natural hazards (CRED, 2018). However, effects due to landslides caused global total annual damage of about 18 billion Euros (Zhu et al., 2018) and approximate 66,438 deaths by 2020 worldwide (Guha-Sapir et al., 2020).
In Africa, landslides occurs almost in every region but more frequently reported in Sierra Leone, DR Congo, and Nigeria (Igwe, 2018), Cameroon (Nama, 2020), Ivory Coast, Kenya, Uganda, Rwanda, and Ethiopia (Knapen et al., 2006; Nyssen et al., 2003). In most highlands of East Africa such as Ruwenzori in Uganda, Elgon in Uganda/Kenya, Aberdare Ranges in Kenya, and Bukavu in the Democratic Republic of Congo landslides are more common (Mugagga et al., 2012) with haphazard impact on the environment and society. In Tanzania, various types of landslides have been widely documented from different regions of the country in lapse of time. This includes large quaternary debris avalanches reported on Mt. Meru and Kilimanjaro (Delcamp et al., 2013), Oldonyo Lengai, and Kerimasi Mountains (Kervyn et al., 2008), Uluguru Mountains (Westerberg & Christiansson, 1999), Rungwe Mountains (Fontijn et al., 2012). Nevertheless, mudflow has been reported in the different time frames at Usambara Mountains, Tanga region, and in Rondo & Makonde Plateaus in southern regions of Tanzania. Rock falls and rock topple are most common landslide events in Lushoto district, north eastern Tanzania reported in different time frames (Tegeje, 2017). Nevertheless, in 1998 landslide hazards destroyed 60 hectares of land with intense impacts to the society (Tegeje, 2017). However, during the rainy season, most landslide events in the area leads to human death and injuries, loss of properties including farm products, soil nutrients swept away by surface runoff, destruction of houses, roads, and time to time electrical cut-off among others. Short-term migration of families and their livestock to secure safe places is a common phenomenon during this period. Severe rainfall, changes in drainage patterns, farming on slopes, and deforestation as a result of settlement growth and the nature of the topography are the agents of landslides in the area, resulting in catastrophic corollaries on society and the environment. However, they have not been effectively and scientifically studied within the area and their spatial distributions are still undefined.
In recent years, several landslides predictive models have been employed to widely map the landslide's susceptibility zones basing on the analysis, scale, modelling approaches, and the causative evaluation factors (Brenning, 2005; Reichenbach et al., 2018). These models consist of a qualitative approach which usually returns a landslide susceptibility zonation in terms of weighted indices and relative ranks (e.g., low, medium, high); adopted for local scale and site-specific studies, and quantitative approaches which estimate landslide occurrence as a numerical value (Roccati et al., 2021). Currently, Geographical Information System (GIS) and remote sensing technologies have been extensively employed and proven effective for landslide susceptibility assessment and mapping worldwide (Dou et al., 2019). These technologies can manage a large volume of datasets both in terms of data volume and the geographical scale, as well as undertake dynamic and ongoing landslides susceptibility zonation representing an important requisite for proper management of land and risk mitigation on the earth’s surface (Lai & Tsai, 2019; Turconi et al., 2019). Often, these technologies combine various datasets collected from innovative techniques such as satellite remote sensing and light detection and ranging (LiDAR) images (Roccati et al., 2021). These technologies are widely integrated with numerous statistical models and approaches which have been suggested and extensively used in landslides susceptibility mapping as engaged in various studies globally including; frequency ratio (FR) (Aditian et al., 2018; Silalahi et al., 2019; Yalcin et al., 2011) index of entropy (Chen et al., 2021; Singh et al., 2021), analytical hierarchy process (AHP) (Jam et al., 2021; Moragues et al., 2021), analytical network process (ANP) (Hamzeh & Amiri, 2020; Swetha & Gopinath, 2020), information value (Sharma et al., 2020; Wubalem & Meten, 2020), fuzzy logic (Baharvand et al., 2020; Bahrami et al., 2020) among others and a hybrid of the aforementioned techniques can be used to overcome biasines (Gheshlaghi & Feizizadeh, 2021). However, for regional and small scale landslides susceptibility mapping, statistical methods are the most and widely frequently adopted techniques (Cascini, 2008; Yalcin et al., 2011). In the current study, AHP has been employed in conjunction with multicriteria decision analysis (MCDA) which allows evaluation of consistency in the assignment of causative weights, hence lessening the subjectivity factor in the treatment of variables. The technique is widely used for analysing complex spatial-related challenges such as site selection, groundwater assessment, ecological studies, flood assessment, landslides susceptibility analysis among others (Biswas et al., 2020; Makonyo & Msabi, 2021a, 2021b; Morandi et al., 2020; Msabi & Makonyo, 2021). The method applies semi-quantitative approaches and the judgment is analysed through weighting of factors by pairwise comparison matrix (Saaty, 1980).
Landslides susceptibility mapping is a complex process that requires expertise from different disciplines include; geology, environmental, hydrogeology among others (Ayalew et al., 2004); and it has been an effective tool for understanding the probability of the spatial distribution of future landslides (Feizizadeh et al., 2014). However, various researchers have integrated various causative factors from different discipline including slope angle, geomorphology, aspect slope, lithology (Moragues et al., 2021), relief, surface roughness, distance to streams (Bostjančić et al., 2021), slope angle, slope aspect, rainfall, lithology, altitude, land use (Jam et al., 2021), lithology, elevation, slope, aspect, curvature, land cover, proximity from drainage networks, lineament and roads, (Ozioko & Igwe, 2020) but in different combination. However, nine influencing factors; slope angle, aspects, elevation, lithology, proximity to rivers, proximity to geological faults, roads, rainfall, and land use/ land cover (LULC) have been employed in this study. Therefore the current study is aimed at identifying landslides suscptibility zones at Lushoto district, North eastern Tanzania based on GIS and MCDA.